import{S as jN,i as AN,s as ON,e as r,k as p,w as k,t as o,M as IN,c as a,d as t,m,a as s,x as T,h as n,b as u,G as e,g,y as w,q as $,o as F,B as P,v as DN,L as Oe}from"../../chunks/vendor-hf-doc-builder.js";import{T as je}from"../../chunks/Tip-hf-doc-builder.js";import{D as W}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Ie}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Ue}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Ae}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function SN(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import RobertaPreLayerNormConfig, RobertaPreLayerNormModel

# Initializing a RoBERTa-PreLayerNorm configuration
configuration = RobertaPreLayerNormConfig()

# Initializing a model (with random weights) from the configuration
model = RobertaPreLayerNormModel(configuration)

# Accessing the model configuration
configuration = model.config`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaPreLayerNormConfig, RobertaPreLayerNormModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a RoBERTa-PreLayerNorm configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = RobertaPreLayerNormConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaPreLayerNormModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){d=r("p"),y=o("Examples:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Examples:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function WN(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function UN(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, RobertaPreLayerNormModel
import torch

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = RobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, RobertaPreLayerNormModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaPreLayerNormModel.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function QN(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function HN(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, RobertaPreLayerNormForCausalLM, AutoConfig
import torch

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
config = AutoConfig.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
config.is_decoder = True
model = RobertaPreLayerNormForCausalLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40", config=config)

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)

prediction_logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, RobertaPreLayerNormForCausalLM, AutoConfig
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config.is_decoder = <span class="hljs-literal">True</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaPreLayerNormForCausalLM.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>, config=config)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>prediction_logits = outputs.logits`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function BN(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function KN(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, RobertaPreLayerNormForMaskedLM
import torch

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = RobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

inputs = tokenizer("The capital of France is <mask>.", return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

# retrieve index of <mask>
mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]

predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
tokenizer.decode(predicted_token_id)

labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
# mask labels of non-<mask> tokens
labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)

outputs = model(**inputs, labels=labels)
round(outputs.loss.item(), 2)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, RobertaPreLayerNormForMaskedLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaPreLayerNormForMaskedLM.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;The capital of France is &lt;mask&gt;.&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># retrieve index of &lt;mask&gt;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[<span class="hljs-number">0</span>].nonzero(as_tuple=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_token_id = logits[<span class="hljs-number">0</span>, mask_token_index].argmax(axis=-<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(predicted_token_id)
<span class="hljs-string">&#x27; Paris&#x27;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tokenizer(<span class="hljs-string">&quot;The capital of France is Paris.&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># mask labels of non-&lt;mask&gt; tokens</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -<span class="hljs-number">100</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, labels=labels)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(outputs.loss.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">0.69</span>`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function VN(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function JN(N){let d,y,h,f,_;return f=new Ie({props:{code:`import torch
from transformers import AutoTokenizer, RobertaPreLayerNormForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = RobertaPreLayerNormForSequenceClassification.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

predicted_class_id = logits.argmax().item()

# To train a model on \`num_labels\` classes, you can pass \`num_labels=num_labels\` to \`.from_pretrained(...)\`
num_labels = len(model.config.id2label)
model = RobertaPreLayerNormForSequenceClassification.from_pretrained("andreasmadsen/efficient_mlm_m0.40", num_labels=num_labels)

labels = torch.tensor([1])
loss = model(**inputs, labels=labels).loss`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, RobertaPreLayerNormForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaPreLayerNormForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_id = logits.argmax().item()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># To train a model on \`num_labels\` classes, you can pass \`num_labels=num_labels\` to \`.from_pretrained(...)\`</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>num_labels = <span class="hljs-built_in">len</span>(model.config.id2label)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaPreLayerNormForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>, num_labels=num_labels)

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = torch.tensor([<span class="hljs-number">1</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss`}}),{c(){d=r("p"),y=o("Example of single-label classification:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example of single-label classification:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function GN(N){let d,y,h,f,_;return f=new Ie({props:{code:`import torch
from transformers import AutoTokenizer, RobertaPreLayerNormForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = RobertaPreLayerNormForSequenceClassification.from_pretrained("andreasmadsen/efficient_mlm_m0.40", problem_type="multi_label_classification")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]

# To train a model on \`num_labels\` classes, you can pass \`num_labels=num_labels\` to \`.from_pretrained(...)\`
num_labels = len(model.config.id2label)
model = RobertaPreLayerNormForSequenceClassification.from_pretrained(
    "andreasmadsen/efficient_mlm_m0.40", num_labels=num_labels, problem_type="multi_label_classification"
)

labels = torch.sum(
    torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
).to(torch.float)
loss = model(**inputs, labels=labels).loss`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, RobertaPreLayerNormForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaPreLayerNormForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>, problem_type=<span class="hljs-string">&quot;multi_label_classification&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_ids = torch.arange(<span class="hljs-number">0</span>, logits.shape[-<span class="hljs-number">1</span>])[torch.sigmoid(logits).squeeze(dim=<span class="hljs-number">0</span>) &gt; <span class="hljs-number">0.5</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># To train a model on \`num_labels\` classes, you can pass \`num_labels=num_labels\` to \`.from_pretrained(...)\`</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>num_labels = <span class="hljs-built_in">len</span>(model.config.id2label)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaPreLayerNormForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>, num_labels=num_labels, problem_type=<span class="hljs-string">&quot;multi_label_classification&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = torch.<span class="hljs-built_in">sum</span>(
<span class="hljs-meta">... </span>    torch.nn.functional.one_hot(predicted_class_ids[<span class="hljs-literal">None</span>, :].clone(), num_classes=num_labels), dim=<span class="hljs-number">1</span>
<span class="hljs-meta">... </span>).to(torch.<span class="hljs-built_in">float</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss`}}),{c(){d=r("p"),y=o("Example of multi-label classification:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example of multi-label classification:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function XN(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function YN(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, RobertaPreLayerNormForMultipleChoice
import torch

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = RobertaPreLayerNormForMultipleChoice.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
choice0 = "It is eaten with a fork and a knife."
choice1 = "It is eaten while held in the hand."
labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1

encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
outputs = model(**{k: v.unsqueeze(0) for k, v in encoding.items()}, labels=labels)  # batch size is 1

# the linear classifier still needs to be trained
loss = outputs.loss
logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, RobertaPreLayerNormForMultipleChoice
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaPreLayerNormForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice0 = <span class="hljs-string">&quot;It is eaten with a fork and a knife.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice1 = <span class="hljs-string">&quot;It is eaten while held in the hand.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = torch.tensor(<span class="hljs-number">0</span>).unsqueeze(<span class="hljs-number">0</span>)  <span class="hljs-comment"># choice0 is correct (according to Wikipedia ;)), batch size 1</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, padding=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**{k: v.unsqueeze(<span class="hljs-number">0</span>) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> encoding.items()}, labels=labels)  <span class="hljs-comment"># batch size is 1</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># the linear classifier still needs to be trained</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function ZN(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function eL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, RobertaPreLayerNormForTokenClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = RobertaPreLayerNormForTokenClassification.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

inputs = tokenizer(
    "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
)

with torch.no_grad():
    logits = model(**inputs).logits

predicted_token_class_ids = logits.argmax(-1)

# Note that tokens are classified rather then input words which means that
# there might be more predicted token classes than words.
# Multiple token classes might account for the same word
predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]

labels = predicted_token_class_ids
loss = model(**inputs, labels=labels).loss`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, RobertaPreLayerNormForTokenClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaPreLayerNormForTokenClassification.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;HuggingFace is a company based in Paris and New York&quot;</span>, add_special_tokens=<span class="hljs-literal">False</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_token_class_ids = logits.argmax(-<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Note that tokens are classified rather then input words which means that</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># there might be more predicted token classes than words.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Multiple token classes might account for the same word</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_tokens_classes = [model.config.id2label[t.item()] <span class="hljs-keyword">for</span> t <span class="hljs-keyword">in</span> predicted_token_class_ids[<span class="hljs-number">0</span>]]

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = predicted_token_class_ids
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function tL(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function oL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, RobertaPreLayerNormForQuestionAnswering
import torch

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = RobertaPreLayerNormForQuestionAnswering.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

inputs = tokenizer(question, text, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

answer_start_index = outputs.start_logits.argmax()
answer_end_index = outputs.end_logits.argmax()

predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]

# target is "nice puppet"
target_start_index = torch.tensor([14])
target_end_index = torch.tensor([15])

outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
loss = outputs.loss`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, RobertaPreLayerNormForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaPreLayerNormForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>question, text = <span class="hljs-string">&quot;Who was Jim Henson?&quot;</span>, <span class="hljs-string">&quot;Jim Henson was a nice puppet&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(question, text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>answer_start_index = outputs.start_logits.argmax()
<span class="hljs-meta">&gt;&gt;&gt; </span>answer_end_index = outputs.end_logits.argmax()

<span class="hljs-meta">&gt;&gt;&gt; </span>predict_answer_tokens = inputs.input_ids[<span class="hljs-number">0</span>, answer_start_index : answer_end_index + <span class="hljs-number">1</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># target is &quot;nice puppet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>target_start_index = torch.tensor([<span class="hljs-number">14</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>target_end_index = torch.tensor([<span class="hljs-number">15</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function nL(N){let d,y,h,f,_,l,c,x,ye,he,D,ae,te,L,be,Q,ve,ue,A,ke,se,K,Te,ce,V,we,ie,J,$e,fe,ee,R,C,le,H,Fe,ge,U,Pe,_e,z,B,G,de,Ne,X,pe,Le,S,me,Y,xe,oe,q,Me,j,Ee,Re;return{c(){d=r("p"),y=o("TensorFlow models and layers in "),h=r("code"),f=o("transformers"),_=o(" accept two formats as input:"),l=p(),c=r("ul"),x=r("li"),ye=o("having all inputs as keyword arguments (like PyTorch models), or"),he=p(),D=r("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),L=r("p"),be=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),Q=r("code"),ve=o("model.fit()"),ue=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),A=r("code"),ke=o("model.fit()"),se=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),K=r("code"),Te=o("fit()"),ce=o(" and "),V=r("code"),we=o("predict()"),ie=o(`, such as when creating your own layers or models with
the Keras `),J=r("code"),$e=o("Functional"),fe=o(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),ee=p(),R=r("ul"),C=r("li"),le=o("a single Tensor with "),H=r("code"),Fe=o("input_ids"),ge=o(" only and nothing else: "),U=r("code"),Pe=o("model(input_ids)"),_e=p(),z=r("li"),B=o(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),G=r("code"),de=o("model([input_ids, attention_mask])"),Ne=o(" or "),X=r("code"),pe=o("model([input_ids, attention_mask, token_type_ids])"),Le=p(),S=r("li"),me=o(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),Y=r("code"),xe=o('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),oe=p(),q=r("p"),Me=o(`Note that when creating models and layers with
`),j=r("a"),Ee=o("subclassing"),Re=o(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(v){d=a(v,"P",{});var M=s(d);y=n(M,"TensorFlow models and layers in "),h=a(M,"CODE",{});var Qe=s(h);f=n(Qe,"transformers"),Qe.forEach(t),_=n(M," accept two formats as input:"),M.forEach(t),l=m(v),c=a(v,"UL",{});var re=s(c);x=a(re,"LI",{});var ze=s(x);ye=n(ze,"having all inputs as keyword arguments (like PyTorch models), or"),ze.forEach(t),he=m(re),D=a(re,"LI",{});var He=s(D);ae=n(He,"having all inputs as a list, tuple or dict in the first positional argument."),He.forEach(t),re.forEach(t),te=m(v),L=a(v,"P",{});var E=s(L);be=n(E,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),Q=a(E,"CODE",{});var Be=s(Q);ve=n(Be,"model.fit()"),Be.forEach(t),ue=n(E,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),A=a(E,"CODE",{});var Ke=s(A);ke=n(Ke,"model.fit()"),Ke.forEach(t),se=n(E,` supports! If, however, you want to use the second
format outside of Keras methods like `),K=a(E,"CODE",{});var De=s(K);Te=n(De,"fit()"),De.forEach(t),ce=n(E," and "),V=a(E,"CODE",{});var Ve=s(V);we=n(Ve,"predict()"),Ve.forEach(t),ie=n(E,`, such as when creating your own layers or models with
the Keras `),J=a(E,"CODE",{});var Je=s(J);$e=n(Je,"Functional"),Je.forEach(t),fe=n(E,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),E.forEach(t),ee=m(v),R=a(v,"UL",{});var O=s(R);C=a(O,"LI",{});var I=s(C);le=n(I,"a single Tensor with "),H=a(I,"CODE",{});var Ge=s(H);Fe=n(Ge,"input_ids"),Ge.forEach(t),ge=n(I," only and nothing else: "),U=a(I,"CODE",{});var Se=s(U);Pe=n(Se,"model(input_ids)"),Se.forEach(t),I.forEach(t),_e=m(O),z=a(O,"LI",{});var Z=s(z);B=n(Z,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),G=a(Z,"CODE",{});var Xe=s(G);de=n(Xe,"model([input_ids, attention_mask])"),Xe.forEach(t),Ne=n(Z," or "),X=a(Z,"CODE",{});var We=s(X);pe=n(We,"model([input_ids, attention_mask, token_type_ids])"),We.forEach(t),Z.forEach(t),Le=m(O),S=a(O,"LI",{});var Ce=s(S);me=n(Ce,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),Y=a(Ce,"CODE",{});var Ye=s(Y);xe=n(Ye,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),Ye.forEach(t),Ce.forEach(t),O.forEach(t),oe=m(v),q=a(v,"P",{});var ne=s(q);Me=n(ne,`Note that when creating models and layers with
`),j=a(ne,"A",{href:!0,rel:!0});var qe=s(j);Ee=n(qe,"subclassing"),qe.forEach(t),Re=n(ne,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),ne.forEach(t),this.h()},h(){u(j,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),u(j,"rel","nofollow")},m(v,M){g(v,d,M),e(d,y),e(d,h),e(h,f),e(d,_),g(v,l,M),g(v,c,M),e(c,x),e(x,ye),e(c,he),e(c,D),e(D,ae),g(v,te,M),g(v,L,M),e(L,be),e(L,Q),e(Q,ve),e(L,ue),e(L,A),e(A,ke),e(L,se),e(L,K),e(K,Te),e(L,ce),e(L,V),e(V,we),e(L,ie),e(L,J),e(J,$e),e(L,fe),g(v,ee,M),g(v,R,M),e(R,C),e(C,le),e(C,H),e(H,Fe),e(C,ge),e(C,U),e(U,Pe),e(R,_e),e(R,z),e(z,B),e(z,G),e(G,de),e(z,Ne),e(z,X),e(X,pe),e(R,Le),e(R,S),e(S,me),e(S,Y),e(Y,xe),g(v,oe,M),g(v,q,M),e(q,Me),e(q,j),e(j,Ee),e(q,Re)},d(v){v&&t(d),v&&t(l),v&&t(c),v&&t(te),v&&t(L),v&&t(ee),v&&t(R),v&&t(oe),v&&t(q)}}}function rL(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function aL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, TFRobertaPreLayerNormModel
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = TFRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
outputs = model(inputs)

last_hidden_states = outputs.last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFRobertaPreLayerNormModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRobertaPreLayerNormModel.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function sL(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function iL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, TFRobertaPreLayerNormForCausalLM
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = TFRobertaPreLayerNormForCausalLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
outputs = model(inputs)
logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFRobertaPreLayerNormForCausalLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRobertaPreLayerNormForCausalLM.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function lL(N){let d,y,h,f,_,l,c,x,ye,he,D,ae,te,L,be,Q,ve,ue,A,ke,se,K,Te,ce,V,we,ie,J,$e,fe,ee,R,C,le,H,Fe,ge,U,Pe,_e,z,B,G,de,Ne,X,pe,Le,S,me,Y,xe,oe,q,Me,j,Ee,Re;return{c(){d=r("p"),y=o("TensorFlow models and layers in "),h=r("code"),f=o("transformers"),_=o(" accept two formats as input:"),l=p(),c=r("ul"),x=r("li"),ye=o("having all inputs as keyword arguments (like PyTorch models), or"),he=p(),D=r("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),L=r("p"),be=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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format outside of Keras methods like `),K=r("code"),Te=o("fit()"),ce=o(" and "),V=r("code"),we=o("predict()"),ie=o(`, such as when creating your own layers or models with
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`),Y=r("code"),xe=o('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),oe=p(),q=r("p"),Me=o(`Note that when creating models and layers with
`),j=r("a"),Ee=o("subclassing"),Re=o(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(v){d=a(v,"P",{});var M=s(d);y=n(M,"TensorFlow models and layers in "),h=a(M,"CODE",{});var Qe=s(h);f=n(Qe,"transformers"),Qe.forEach(t),_=n(M," accept two formats as input:"),M.forEach(t),l=m(v),c=a(v,"UL",{});var re=s(c);x=a(re,"LI",{});var ze=s(x);ye=n(ze,"having all inputs as keyword arguments (like PyTorch models), or"),ze.forEach(t),he=m(re),D=a(re,"LI",{});var He=s(D);ae=n(He,"having all inputs as a list, tuple or dict in the first positional argument."),He.forEach(t),re.forEach(t),te=m(v),L=a(v,"P",{});var E=s(L);be=n(E,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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pass your inputs and labels in any format that `),A=a(E,"CODE",{});var Ke=s(A);ke=n(Ke,"model.fit()"),Ke.forEach(t),se=n(E,` supports! If, however, you want to use the second
format outside of Keras methods like `),K=a(E,"CODE",{});var De=s(K);Te=n(De,"fit()"),De.forEach(t),ce=n(E," and "),V=a(E,"CODE",{});var Ve=s(V);we=n(Ve,"predict()"),Ve.forEach(t),ie=n(E,`, such as when creating your own layers or models with
the Keras `),J=a(E,"CODE",{});var Je=s(J);$e=n(Je,"Functional"),Je.forEach(t),fe=n(E,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),E.forEach(t),ee=m(v),R=a(v,"UL",{});var O=s(R);C=a(O,"LI",{});var I=s(C);le=n(I,"a single Tensor with "),H=a(I,"CODE",{});var Ge=s(H);Fe=n(Ge,"input_ids"),Ge.forEach(t),ge=n(I," only and nothing else: "),U=a(I,"CODE",{});var Se=s(U);Pe=n(Se,"model(input_ids)"),Se.forEach(t),I.forEach(t),_e=m(O),z=a(O,"LI",{});var Z=s(z);B=n(Z,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),G=a(Z,"CODE",{});var Xe=s(G);de=n(Xe,"model([input_ids, attention_mask])"),Xe.forEach(t),Ne=n(Z," or "),X=a(Z,"CODE",{});var We=s(X);pe=n(We,"model([input_ids, attention_mask, token_type_ids])"),We.forEach(t),Z.forEach(t),Le=m(O),S=a(O,"LI",{});var Ce=s(S);me=n(Ce,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),Y=a(Ce,"CODE",{});var Ye=s(Y);xe=n(Ye,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),Ye.forEach(t),Ce.forEach(t),O.forEach(t),oe=m(v),q=a(v,"P",{});var ne=s(q);Me=n(ne,`Note that when creating models and layers with
`),j=a(ne,"A",{href:!0,rel:!0});var qe=s(j);Ee=n(qe,"subclassing"),qe.forEach(t),Re=n(ne,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),ne.forEach(t),this.h()},h(){u(j,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),u(j,"rel","nofollow")},m(v,M){g(v,d,M),e(d,y),e(d,h),e(h,f),e(d,_),g(v,l,M),g(v,c,M),e(c,x),e(x,ye),e(c,he),e(c,D),e(D,ae),g(v,te,M),g(v,L,M),e(L,be),e(L,Q),e(Q,ve),e(L,ue),e(L,A),e(A,ke),e(L,se),e(L,K),e(K,Te),e(L,ce),e(L,V),e(V,we),e(L,ie),e(L,J),e(J,$e),e(L,fe),g(v,ee,M),g(v,R,M),e(R,C),e(C,le),e(C,H),e(H,Fe),e(C,ge),e(C,U),e(U,Pe),e(R,_e),e(R,z),e(z,B),e(z,G),e(G,de),e(z,Ne),e(z,X),e(X,pe),e(R,Le),e(R,S),e(S,me),e(S,Y),e(Y,xe),g(v,oe,M),g(v,q,M),e(q,Me),e(q,j),e(j,Ee),e(q,Re)},d(v){v&&t(d),v&&t(l),v&&t(c),v&&t(te),v&&t(L),v&&t(ee),v&&t(R),v&&t(oe),v&&t(q)}}}function dL(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function cL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, TFRobertaPreLayerNormForMaskedLM
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = TFRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

inputs = tokenizer("The capital of France is <mask>.", return_tensors="tf")
logits = model(**inputs).logits

# retrieve index of <mask>
mask_token_index = tf.where((inputs.input_ids == tokenizer.mask_token_id)[0])
selected_logits = tf.gather_nd(logits[0], indices=mask_token_index)

predicted_token_id = tf.math.argmax(selected_logits, axis=-1)
tokenizer.decode(predicted_token_id)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFRobertaPreLayerNormForMaskedLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRobertaPreLayerNormForMaskedLM.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;The capital of France is &lt;mask&gt;.&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># retrieve index of &lt;mask&gt;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>mask_token_index = tf.where((inputs.input_ids == tokenizer.mask_token_id)[<span class="hljs-number">0</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>selected_logits = tf.gather_nd(logits[<span class="hljs-number">0</span>], indices=mask_token_index)

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_token_id = tf.math.argmax(selected_logits, axis=-<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(predicted_token_id)
<span class="hljs-string">&#x27; Paris&#x27;</span>`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function pL(N){let d,y;return d=new Ie({props:{code:`labels = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
# mask labels of non-<mask> tokens
labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)

outputs = model(**inputs, labels=labels)
round(float(outputs.loss), 2)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tokenizer(<span class="hljs-string">&quot;The capital of France is Paris.&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># mask labels of non-&lt;mask&gt; tokens</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -<span class="hljs-number">100</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, labels=labels)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(<span class="hljs-built_in">float</span>(outputs.loss), <span class="hljs-number">2</span>)
<span class="hljs-number">0.69</span>`}}),{c(){k(d.$$.fragment)},l(h){T(d.$$.fragment,h)},m(h,f){w(d,h,f),y=!0},p:Oe,i(h){y||($(d.$$.fragment,h),y=!0)},o(h){F(d.$$.fragment,h),y=!1},d(h){P(d,h)}}}function mL(N){let d,y,h,f,_,l,c,x,ye,he,D,ae,te,L,be,Q,ve,ue,A,ke,se,K,Te,ce,V,we,ie,J,$e,fe,ee,R,C,le,H,Fe,ge,U,Pe,_e,z,B,G,de,Ne,X,pe,Le,S,me,Y,xe,oe,q,Me,j,Ee,Re;return{c(){d=r("p"),y=o("TensorFlow models and layers in "),h=r("code"),f=o("transformers"),_=o(" accept two formats as input:"),l=p(),c=r("ul"),x=r("li"),ye=o("having all inputs as keyword arguments (like PyTorch models), or"),he=p(),D=r("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),L=r("p"),be=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),Q=r("code"),ve=o("model.fit()"),ue=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),A=r("code"),ke=o("model.fit()"),se=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),K=r("code"),Te=o("fit()"),ce=o(" and "),V=r("code"),we=o("predict()"),ie=o(`, such as when creating your own layers or models with
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positional argument:`),ee=p(),R=r("ul"),C=r("li"),le=o("a single Tensor with "),H=r("code"),Fe=o("input_ids"),ge=o(" only and nothing else: "),U=r("code"),Pe=o("model(input_ids)"),_e=p(),z=r("li"),B=o(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
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`),j=r("a"),Ee=o("subclassing"),Re=o(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(v){d=a(v,"P",{});var M=s(d);y=n(M,"TensorFlow models and layers in "),h=a(M,"CODE",{});var Qe=s(h);f=n(Qe,"transformers"),Qe.forEach(t),_=n(M," accept two formats as input:"),M.forEach(t),l=m(v),c=a(v,"UL",{});var re=s(c);x=a(re,"LI",{});var ze=s(x);ye=n(ze,"having all inputs as keyword arguments (like PyTorch models), or"),ze.forEach(t),he=m(re),D=a(re,"LI",{});var He=s(D);ae=n(He,"having all inputs as a list, tuple or dict in the first positional argument."),He.forEach(t),re.forEach(t),te=m(v),L=a(v,"P",{});var E=s(L);be=n(E,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),Q=a(E,"CODE",{});var Be=s(Q);ve=n(Be,"model.fit()"),Be.forEach(t),ue=n(E,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),A=a(E,"CODE",{});var Ke=s(A);ke=n(Ke,"model.fit()"),Ke.forEach(t),se=n(E,` supports! If, however, you want to use the second
format outside of Keras methods like `),K=a(E,"CODE",{});var De=s(K);Te=n(De,"fit()"),De.forEach(t),ce=n(E," and "),V=a(E,"CODE",{});var Ve=s(V);we=n(Ve,"predict()"),Ve.forEach(t),ie=n(E,`, such as when creating your own layers or models with
the Keras `),J=a(E,"CODE",{});var Je=s(J);$e=n(Je,"Functional"),Je.forEach(t),fe=n(E,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),E.forEach(t),ee=m(v),R=a(v,"UL",{});var O=s(R);C=a(O,"LI",{});var I=s(C);le=n(I,"a single Tensor with "),H=a(I,"CODE",{});var Ge=s(H);Fe=n(Ge,"input_ids"),Ge.forEach(t),ge=n(I," only and nothing else: "),U=a(I,"CODE",{});var Se=s(U);Pe=n(Se,"model(input_ids)"),Se.forEach(t),I.forEach(t),_e=m(O),z=a(O,"LI",{});var Z=s(z);B=n(Z,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),G=a(Z,"CODE",{});var Xe=s(G);de=n(Xe,"model([input_ids, attention_mask])"),Xe.forEach(t),Ne=n(Z," or "),X=a(Z,"CODE",{});var We=s(X);pe=n(We,"model([input_ids, attention_mask, token_type_ids])"),We.forEach(t),Z.forEach(t),Le=m(O),S=a(O,"LI",{});var Ce=s(S);me=n(Ce,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),Y=a(Ce,"CODE",{});var Ye=s(Y);xe=n(Ye,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),Ye.forEach(t),Ce.forEach(t),O.forEach(t),oe=m(v),q=a(v,"P",{});var ne=s(q);Me=n(ne,`Note that when creating models and layers with
`),j=a(ne,"A",{href:!0,rel:!0});var qe=s(j);Ee=n(qe,"subclassing"),qe.forEach(t),Re=n(ne,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),ne.forEach(t),this.h()},h(){u(j,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),u(j,"rel","nofollow")},m(v,M){g(v,d,M),e(d,y),e(d,h),e(h,f),e(d,_),g(v,l,M),g(v,c,M),e(c,x),e(x,ye),e(c,he),e(c,D),e(D,ae),g(v,te,M),g(v,L,M),e(L,be),e(L,Q),e(Q,ve),e(L,ue),e(L,A),e(A,ke),e(L,se),e(L,K),e(K,Te),e(L,ce),e(L,V),e(V,we),e(L,ie),e(L,J),e(J,$e),e(L,fe),g(v,ee,M),g(v,R,M),e(R,C),e(C,le),e(C,H),e(H,Fe),e(C,ge),e(C,U),e(U,Pe),e(R,_e),e(R,z),e(z,B),e(z,G),e(G,de),e(z,Ne),e(z,X),e(X,pe),e(R,Le),e(R,S),e(S,me),e(S,Y),e(Y,xe),g(v,oe,M),g(v,q,M),e(q,Me),e(q,j),e(j,Ee),e(q,Re)},d(v){v&&t(d),v&&t(l),v&&t(c),v&&t(te),v&&t(L),v&&t(ee),v&&t(R),v&&t(oe),v&&t(q)}}}function hL(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function uL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, TFRobertaPreLayerNormForSequenceClassification
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = TFRobertaPreLayerNormForSequenceClassification.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")

logits = model(**inputs).logits

predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFRobertaPreLayerNormForSequenceClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRobertaPreLayerNormForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_id = <span class="hljs-built_in">int</span>(tf.math.argmax(logits, axis=-<span class="hljs-number">1</span>)[<span class="hljs-number">0</span>])`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function fL(N){let d,y;return d=new Ie({props:{code:'# To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`\nnum_labels = len(model.config.id2label)\nmodel = TFRobertaPreLayerNormForSequenceClassification.from_pretrained("andreasmadsen/efficient_mlm_m0.40", num_labels=num_labels)\n\nlabels = tf.constant(1)\nloss = model(**inputs, labels=labels).loss',highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`</span>\n<span class="hljs-meta">&gt;&gt;&gt; </span>num_labels = <span class="hljs-built_in">len</span>(model.config.id2label)\n<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRobertaPreLayerNormForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>, num_labels=num_labels)\n\n<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tf.constant(<span class="hljs-number">1</span>)\n<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss'}}),{c(){k(d.$$.fragment)},l(h){T(d.$$.fragment,h)},m(h,f){w(d,h,f),y=!0},p:Oe,i(h){y||($(d.$$.fragment,h),y=!0)},o(h){F(d.$$.fragment,h),y=!1},d(h){P(d,h)}}}function gL(N){let d,y,h,f,_,l,c,x,ye,he,D,ae,te,L,be,Q,ve,ue,A,ke,se,K,Te,ce,V,we,ie,J,$e,fe,ee,R,C,le,H,Fe,ge,U,Pe,_e,z,B,G,de,Ne,X,pe,Le,S,me,Y,xe,oe,q,Me,j,Ee,Re;return{c(){d=r("p"),y=o("TensorFlow models and layers in "),h=r("code"),f=o("transformers"),_=o(" accept two formats as input:"),l=p(),c=r("ul"),x=r("li"),ye=o("having all inputs as keyword arguments (like PyTorch models), or"),he=p(),D=r("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),L=r("p"),be=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),Q=r("code"),ve=o("model.fit()"),ue=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),A=r("code"),ke=o("model.fit()"),se=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),K=r("code"),Te=o("fit()"),ce=o(" and "),V=r("code"),we=o("predict()"),ie=o(`, such as when creating your own layers or models with
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positional argument:`),ee=p(),R=r("ul"),C=r("li"),le=o("a single Tensor with "),H=r("code"),Fe=o("input_ids"),ge=o(" only and nothing else: "),U=r("code"),Pe=o("model(input_ids)"),_e=p(),z=r("li"),B=o(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
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`),Y=r("code"),xe=o('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),oe=p(),q=r("p"),Me=o(`Note that when creating models and layers with
`),j=r("a"),Ee=o("subclassing"),Re=o(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(v){d=a(v,"P",{});var M=s(d);y=n(M,"TensorFlow models and layers in "),h=a(M,"CODE",{});var Qe=s(h);f=n(Qe,"transformers"),Qe.forEach(t),_=n(M," accept two formats as input:"),M.forEach(t),l=m(v),c=a(v,"UL",{});var re=s(c);x=a(re,"LI",{});var ze=s(x);ye=n(ze,"having all inputs as keyword arguments (like PyTorch models), or"),ze.forEach(t),he=m(re),D=a(re,"LI",{});var He=s(D);ae=n(He,"having all inputs as a list, tuple or dict in the first positional argument."),He.forEach(t),re.forEach(t),te=m(v),L=a(v,"P",{});var E=s(L);be=n(E,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),Q=a(E,"CODE",{});var Be=s(Q);ve=n(Be,"model.fit()"),Be.forEach(t),ue=n(E,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),A=a(E,"CODE",{});var Ke=s(A);ke=n(Ke,"model.fit()"),Ke.forEach(t),se=n(E,` supports! If, however, you want to use the second
format outside of Keras methods like `),K=a(E,"CODE",{});var De=s(K);Te=n(De,"fit()"),De.forEach(t),ce=n(E," and "),V=a(E,"CODE",{});var Ve=s(V);we=n(Ve,"predict()"),Ve.forEach(t),ie=n(E,`, such as when creating your own layers or models with
the Keras `),J=a(E,"CODE",{});var Je=s(J);$e=n(Je,"Functional"),Je.forEach(t),fe=n(E,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),E.forEach(t),ee=m(v),R=a(v,"UL",{});var O=s(R);C=a(O,"LI",{});var I=s(C);le=n(I,"a single Tensor with "),H=a(I,"CODE",{});var Ge=s(H);Fe=n(Ge,"input_ids"),Ge.forEach(t),ge=n(I," only and nothing else: "),U=a(I,"CODE",{});var Se=s(U);Pe=n(Se,"model(input_ids)"),Se.forEach(t),I.forEach(t),_e=m(O),z=a(O,"LI",{});var Z=s(z);B=n(Z,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),G=a(Z,"CODE",{});var Xe=s(G);de=n(Xe,"model([input_ids, attention_mask])"),Xe.forEach(t),Ne=n(Z," or "),X=a(Z,"CODE",{});var We=s(X);pe=n(We,"model([input_ids, attention_mask, token_type_ids])"),We.forEach(t),Z.forEach(t),Le=m(O),S=a(O,"LI",{});var Ce=s(S);me=n(Ce,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),Y=a(Ce,"CODE",{});var Ye=s(Y);xe=n(Ye,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),Ye.forEach(t),Ce.forEach(t),O.forEach(t),oe=m(v),q=a(v,"P",{});var ne=s(q);Me=n(ne,`Note that when creating models and layers with
`),j=a(ne,"A",{href:!0,rel:!0});var qe=s(j);Ee=n(qe,"subclassing"),qe.forEach(t),Re=n(ne,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),ne.forEach(t),this.h()},h(){u(j,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),u(j,"rel","nofollow")},m(v,M){g(v,d,M),e(d,y),e(d,h),e(h,f),e(d,_),g(v,l,M),g(v,c,M),e(c,x),e(x,ye),e(c,he),e(c,D),e(D,ae),g(v,te,M),g(v,L,M),e(L,be),e(L,Q),e(Q,ve),e(L,ue),e(L,A),e(A,ke),e(L,se),e(L,K),e(K,Te),e(L,ce),e(L,V),e(V,we),e(L,ie),e(L,J),e(J,$e),e(L,fe),g(v,ee,M),g(v,R,M),e(R,C),e(C,le),e(C,H),e(H,Fe),e(C,ge),e(C,U),e(U,Pe),e(R,_e),e(R,z),e(z,B),e(z,G),e(G,de),e(z,Ne),e(z,X),e(X,pe),e(R,Le),e(R,S),e(S,me),e(S,Y),e(Y,xe),g(v,oe,M),g(v,q,M),e(q,Me),e(q,j),e(j,Ee),e(q,Re)},d(v){v&&t(d),v&&t(l),v&&t(c),v&&t(te),v&&t(L),v&&t(ee),v&&t(R),v&&t(oe),v&&t(q)}}}function _L(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function yL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, TFRobertaPreLayerNormForMultipleChoice
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = TFRobertaPreLayerNormForMultipleChoice.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
choice0 = "It is eaten with a fork and a knife."
choice1 = "It is eaten while held in the hand."

encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="tf", padding=True)
inputs = {k: tf.expand_dims(v, 0) for k, v in encoding.items()}
outputs = model(inputs)  # batch size is 1

# the linear classifier still needs to be trained
logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFRobertaPreLayerNormForMultipleChoice
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRobertaPreLayerNormForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice0 = <span class="hljs-string">&quot;It is eaten with a fork and a knife.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice1 = <span class="hljs-string">&quot;It is eaten while held in the hand.&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors=<span class="hljs-string">&quot;tf&quot;</span>, padding=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = {k: tf.expand_dims(v, <span class="hljs-number">0</span>) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> encoding.items()}
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(inputs)  <span class="hljs-comment"># batch size is 1</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># the linear classifier still needs to be trained</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function bL(N){let d,y,h,f,_,l,c,x,ye,he,D,ae,te,L,be,Q,ve,ue,A,ke,se,K,Te,ce,V,we,ie,J,$e,fe,ee,R,C,le,H,Fe,ge,U,Pe,_e,z,B,G,de,Ne,X,pe,Le,S,me,Y,xe,oe,q,Me,j,Ee,Re;return{c(){d=r("p"),y=o("TensorFlow models and layers in "),h=r("code"),f=o("transformers"),_=o(" accept two formats as input:"),l=p(),c=r("ul"),x=r("li"),ye=o("having all inputs as keyword arguments (like PyTorch models), or"),he=p(),D=r("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),L=r("p"),be=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),Q=r("code"),ve=o("model.fit()"),ue=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),A=r("code"),ke=o("model.fit()"),se=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),K=r("code"),Te=o("fit()"),ce=o(" and "),V=r("code"),we=o("predict()"),ie=o(`, such as when creating your own layers or models with
the Keras `),J=r("code"),$e=o("Functional"),fe=o(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),ee=p(),R=r("ul"),C=r("li"),le=o("a single Tensor with "),H=r("code"),Fe=o("input_ids"),ge=o(" only and nothing else: "),U=r("code"),Pe=o("model(input_ids)"),_e=p(),z=r("li"),B=o(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),G=r("code"),de=o("model([input_ids, attention_mask])"),Ne=o(" or "),X=r("code"),pe=o("model([input_ids, attention_mask, token_type_ids])"),Le=p(),S=r("li"),me=o(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),Y=r("code"),xe=o('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),oe=p(),q=r("p"),Me=o(`Note that when creating models and layers with
`),j=r("a"),Ee=o("subclassing"),Re=o(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(v){d=a(v,"P",{});var M=s(d);y=n(M,"TensorFlow models and layers in "),h=a(M,"CODE",{});var Qe=s(h);f=n(Qe,"transformers"),Qe.forEach(t),_=n(M," accept two formats as input:"),M.forEach(t),l=m(v),c=a(v,"UL",{});var re=s(c);x=a(re,"LI",{});var ze=s(x);ye=n(ze,"having all inputs as keyword arguments (like PyTorch models), or"),ze.forEach(t),he=m(re),D=a(re,"LI",{});var He=s(D);ae=n(He,"having all inputs as a list, tuple or dict in the first positional argument."),He.forEach(t),re.forEach(t),te=m(v),L=a(v,"P",{});var E=s(L);be=n(E,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),Q=a(E,"CODE",{});var Be=s(Q);ve=n(Be,"model.fit()"),Be.forEach(t),ue=n(E,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),A=a(E,"CODE",{});var Ke=s(A);ke=n(Ke,"model.fit()"),Ke.forEach(t),se=n(E,` supports! If, however, you want to use the second
format outside of Keras methods like `),K=a(E,"CODE",{});var De=s(K);Te=n(De,"fit()"),De.forEach(t),ce=n(E," and "),V=a(E,"CODE",{});var Ve=s(V);we=n(Ve,"predict()"),Ve.forEach(t),ie=n(E,`, such as when creating your own layers or models with
the Keras `),J=a(E,"CODE",{});var Je=s(J);$e=n(Je,"Functional"),Je.forEach(t),fe=n(E,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),E.forEach(t),ee=m(v),R=a(v,"UL",{});var O=s(R);C=a(O,"LI",{});var I=s(C);le=n(I,"a single Tensor with "),H=a(I,"CODE",{});var Ge=s(H);Fe=n(Ge,"input_ids"),Ge.forEach(t),ge=n(I," only and nothing else: "),U=a(I,"CODE",{});var Se=s(U);Pe=n(Se,"model(input_ids)"),Se.forEach(t),I.forEach(t),_e=m(O),z=a(O,"LI",{});var Z=s(z);B=n(Z,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),G=a(Z,"CODE",{});var Xe=s(G);de=n(Xe,"model([input_ids, attention_mask])"),Xe.forEach(t),Ne=n(Z," or "),X=a(Z,"CODE",{});var We=s(X);pe=n(We,"model([input_ids, attention_mask, token_type_ids])"),We.forEach(t),Z.forEach(t),Le=m(O),S=a(O,"LI",{});var Ce=s(S);me=n(Ce,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),Y=a(Ce,"CODE",{});var Ye=s(Y);xe=n(Ye,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),Ye.forEach(t),Ce.forEach(t),O.forEach(t),oe=m(v),q=a(v,"P",{});var ne=s(q);Me=n(ne,`Note that when creating models and layers with
`),j=a(ne,"A",{href:!0,rel:!0});var qe=s(j);Ee=n(qe,"subclassing"),qe.forEach(t),Re=n(ne,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),ne.forEach(t),this.h()},h(){u(j,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),u(j,"rel","nofollow")},m(v,M){g(v,d,M),e(d,y),e(d,h),e(h,f),e(d,_),g(v,l,M),g(v,c,M),e(c,x),e(x,ye),e(c,he),e(c,D),e(D,ae),g(v,te,M),g(v,L,M),e(L,be),e(L,Q),e(Q,ve),e(L,ue),e(L,A),e(A,ke),e(L,se),e(L,K),e(K,Te),e(L,ce),e(L,V),e(V,we),e(L,ie),e(L,J),e(J,$e),e(L,fe),g(v,ee,M),g(v,R,M),e(R,C),e(C,le),e(C,H),e(H,Fe),e(C,ge),e(C,U),e(U,Pe),e(R,_e),e(R,z),e(z,B),e(z,G),e(G,de),e(z,Ne),e(z,X),e(X,pe),e(R,Le),e(R,S),e(S,me),e(S,Y),e(Y,xe),g(v,oe,M),g(v,q,M),e(q,Me),e(q,j),e(j,Ee),e(q,Re)},d(v){v&&t(d),v&&t(l),v&&t(c),v&&t(te),v&&t(L),v&&t(ee),v&&t(R),v&&t(oe),v&&t(q)}}}function vL(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function kL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, TFRobertaPreLayerNormForTokenClassification
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = TFRobertaPreLayerNormForTokenClassification.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

inputs = tokenizer(
    "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="tf"
)

logits = model(**inputs).logits
predicted_token_class_ids = tf.math.argmax(logits, axis=-1)

# Note that tokens are classified rather then input words which means that
# there might be more predicted token classes than words.
# Multiple token classes might account for the same word
predicted_tokens_classes = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFRobertaPreLayerNormForTokenClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRobertaPreLayerNormForTokenClassification.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;HuggingFace is a company based in Paris and New York&quot;</span>, add_special_tokens=<span class="hljs-literal">False</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = model(**inputs).logits
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_token_class_ids = tf.math.argmax(logits, axis=-<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Note that tokens are classified rather then input words which means that</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># there might be more predicted token classes than words.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Multiple token classes might account for the same word</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_tokens_classes = [model.config.id2label[t] <span class="hljs-keyword">for</span> t <span class="hljs-keyword">in</span> predicted_token_class_ids[<span class="hljs-number">0</span>].numpy().tolist()]`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function TL(N){let d,y;return d=new Ie({props:{code:`labels = predicted_token_class_ids
loss = tf.math.reduce_mean(model(**inputs, labels=labels).loss)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>labels = predicted_token_class_ids
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = tf.math.reduce_mean(model(**inputs, labels=labels).loss)`}}),{c(){k(d.$$.fragment)},l(h){T(d.$$.fragment,h)},m(h,f){w(d,h,f),y=!0},p:Oe,i(h){y||($(d.$$.fragment,h),y=!0)},o(h){F(d.$$.fragment,h),y=!1},d(h){P(d,h)}}}function wL(N){let d,y,h,f,_,l,c,x,ye,he,D,ae,te,L,be,Q,ve,ue,A,ke,se,K,Te,ce,V,we,ie,J,$e,fe,ee,R,C,le,H,Fe,ge,U,Pe,_e,z,B,G,de,Ne,X,pe,Le,S,me,Y,xe,oe,q,Me,j,Ee,Re;return{c(){d=r("p"),y=o("TensorFlow models and layers in "),h=r("code"),f=o("transformers"),_=o(" accept two formats as input:"),l=p(),c=r("ul"),x=r("li"),ye=o("having all inputs as keyword arguments (like PyTorch models), or"),he=p(),D=r("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),L=r("p"),be=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),Q=r("code"),ve=o("model.fit()"),ue=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),A=r("code"),ke=o("model.fit()"),se=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),K=r("code"),Te=o("fit()"),ce=o(" and "),V=r("code"),we=o("predict()"),ie=o(`, such as when creating your own layers or models with
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positional argument:`),ee=p(),R=r("ul"),C=r("li"),le=o("a single Tensor with "),H=r("code"),Fe=o("input_ids"),ge=o(" only and nothing else: "),U=r("code"),Pe=o("model(input_ids)"),_e=p(),z=r("li"),B=o(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),G=r("code"),de=o("model([input_ids, attention_mask])"),Ne=o(" or "),X=r("code"),pe=o("model([input_ids, attention_mask, token_type_ids])"),Le=p(),S=r("li"),me=o(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),Y=r("code"),xe=o('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),oe=p(),q=r("p"),Me=o(`Note that when creating models and layers with
`),j=r("a"),Ee=o("subclassing"),Re=o(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(v){d=a(v,"P",{});var M=s(d);y=n(M,"TensorFlow models and layers in "),h=a(M,"CODE",{});var Qe=s(h);f=n(Qe,"transformers"),Qe.forEach(t),_=n(M," accept two formats as input:"),M.forEach(t),l=m(v),c=a(v,"UL",{});var re=s(c);x=a(re,"LI",{});var ze=s(x);ye=n(ze,"having all inputs as keyword arguments (like PyTorch models), or"),ze.forEach(t),he=m(re),D=a(re,"LI",{});var He=s(D);ae=n(He,"having all inputs as a list, tuple or dict in the first positional argument."),He.forEach(t),re.forEach(t),te=m(v),L=a(v,"P",{});var E=s(L);be=n(E,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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pass your inputs and labels in any format that `),A=a(E,"CODE",{});var Ke=s(A);ke=n(Ke,"model.fit()"),Ke.forEach(t),se=n(E,` supports! If, however, you want to use the second
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`),Y=a(Ce,"CODE",{});var Ye=s(Y);xe=n(Ye,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),Ye.forEach(t),Ce.forEach(t),O.forEach(t),oe=m(v),q=a(v,"P",{});var ne=s(q);Me=n(ne,`Note that when creating models and layers with
`),j=a(ne,"A",{href:!0,rel:!0});var qe=s(j);Ee=n(qe,"subclassing"),qe.forEach(t),Re=n(ne,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),ne.forEach(t),this.h()},h(){u(j,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),u(j,"rel","nofollow")},m(v,M){g(v,d,M),e(d,y),e(d,h),e(h,f),e(d,_),g(v,l,M),g(v,c,M),e(c,x),e(x,ye),e(c,he),e(c,D),e(D,ae),g(v,te,M),g(v,L,M),e(L,be),e(L,Q),e(Q,ve),e(L,ue),e(L,A),e(A,ke),e(L,se),e(L,K),e(K,Te),e(L,ce),e(L,V),e(V,we),e(L,ie),e(L,J),e(J,$e),e(L,fe),g(v,ee,M),g(v,R,M),e(R,C),e(C,le),e(C,H),e(H,Fe),e(C,ge),e(C,U),e(U,Pe),e(R,_e),e(R,z),e(z,B),e(z,G),e(G,de),e(z,Ne),e(z,X),e(X,pe),e(R,Le),e(R,S),e(S,me),e(S,Y),e(Y,xe),g(v,oe,M),g(v,q,M),e(q,Me),e(q,j),e(j,Ee),e(q,Re)},d(v){v&&t(d),v&&t(l),v&&t(c),v&&t(te),v&&t(L),v&&t(ee),v&&t(R),v&&t(oe),v&&t(q)}}}function $L(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function FL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, TFRobertaPreLayerNormForQuestionAnswering
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = TFRobertaPreLayerNormForQuestionAnswering.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

inputs = tokenizer(question, text, return_tensors="tf")
outputs = model(**inputs)

answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])

predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFRobertaPreLayerNormForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRobertaPreLayerNormForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>question, text = <span class="hljs-string">&quot;Who was Jim Henson?&quot;</span>, <span class="hljs-string">&quot;Jim Henson was a nice puppet&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(question, text, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>answer_start_index = <span class="hljs-built_in">int</span>(tf.math.argmax(outputs.start_logits, axis=-<span class="hljs-number">1</span>)[<span class="hljs-number">0</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>answer_end_index = <span class="hljs-built_in">int</span>(tf.math.argmax(outputs.end_logits, axis=-<span class="hljs-number">1</span>)[<span class="hljs-number">0</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span>predict_answer_tokens = inputs.input_ids[<span class="hljs-number">0</span>, answer_start_index : answer_end_index + <span class="hljs-number">1</span>]`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function PL(N){let d,y;return d=new Ie({props:{code:`# target is "nice puppet"
target_start_index = tf.constant([14])
target_end_index = tf.constant([15])

outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
loss = tf.math.reduce_mean(outputs.loss)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># target is &quot;nice puppet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>target_start_index = tf.constant([<span class="hljs-number">14</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>target_end_index = tf.constant([<span class="hljs-number">15</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = tf.math.reduce_mean(outputs.loss)`}}),{c(){k(d.$$.fragment)},l(h){T(d.$$.fragment,h)},m(h,f){w(d,h,f),y=!0},p:Oe,i(h){y||($(d.$$.fragment,h),y=!0)},o(h){F(d.$$.fragment,h),y=!1},d(h){P(d,h)}}}function NL(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function LL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, FlaxRobertaPreLayerNormModel

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxRobertaPreLayerNormModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRobertaPreLayerNormModel.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function xL(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function ML(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, FlaxRobertaPreLayerNormForCausalLM

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = FlaxRobertaPreLayerNormForCausalLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
outputs = model(**inputs)

# retrieve logts for next token
next_token_logits = outputs.logits[:, -1]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxRobertaPreLayerNormForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRobertaPreLayerNormForCausalLM.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># retrieve logts for next token</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>next_token_logits = outputs.logits[:, -<span class="hljs-number">1</span>]`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function EL(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function RL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, FlaxRobertaPreLayerNormForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

inputs = tokenizer("The capital of France is [MASK].", return_tensors="jax")

outputs = model(**inputs)
logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxRobertaPreLayerNormForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;The capital of France is [MASK].&quot;</span>, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function zL(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function CL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, FlaxRobertaPreLayerNormForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = FlaxRobertaPreLayerNormForSequenceClassification.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")

outputs = model(**inputs)
logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxRobertaPreLayerNormForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRobertaPreLayerNormForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function qL(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function jL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, FlaxRobertaPreLayerNormForMultipleChoice

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = FlaxRobertaPreLayerNormForMultipleChoice.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
choice0 = "It is eaten with a fork and a knife."
choice1 = "It is eaten while held in the hand."

encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="jax", padding=True)
outputs = model(**{k: v[None, :] for k, v in encoding.items()})

logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxRobertaPreLayerNormForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRobertaPreLayerNormForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice0 = <span class="hljs-string">&quot;It is eaten with a fork and a knife.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice1 = <span class="hljs-string">&quot;It is eaten while held in the hand.&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors=<span class="hljs-string">&quot;jax&quot;</span>, padding=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**{k: v[<span class="hljs-literal">None</span>, :] <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> encoding.items()})

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function AL(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function OL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, FlaxRobertaPreLayerNormForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = FlaxRobertaPreLayerNormForTokenClassification.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")

outputs = model(**inputs)
logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxRobertaPreLayerNormForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRobertaPreLayerNormForTokenClassification.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function IL(N){let d,y,h,f,_;return{c(){d=r("p"),y=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r("code"),f=o("Module"),_=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=a(c,"CODE",{});var x=s(h);f=n(x,"Module"),x.forEach(t),_=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){g(l,d,c),e(d,y),e(d,h),e(h,f),e(d,_)},d(l){l&&t(d)}}}function DL(N){let d,y,h,f,_;return f=new Ie({props:{code:`from transformers import AutoTokenizer, FlaxRobertaPreLayerNormForQuestionAnswering

tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
model = FlaxRobertaPreLayerNormForQuestionAnswering.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
inputs = tokenizer(question, text, return_tensors="jax")

outputs = model(**inputs)
start_scores = outputs.start_logits
end_scores = outputs.end_logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxRobertaPreLayerNormForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRobertaPreLayerNormForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;andreasmadsen/efficient_mlm_m0.40&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>question, text = <span class="hljs-string">&quot;Who was Jim Henson?&quot;</span>, <span class="hljs-string">&quot;Jim Henson was a nice puppet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(question, text, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>start_scores = outputs.start_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>end_scores = outputs.end_logits`}}),{c(){d=r("p"),y=o("Example:"),h=p(),k(f.$$.fragment)},l(l){d=a(l,"P",{});var c=s(d);y=n(c,"Example:"),c.forEach(t),h=m(l),T(f.$$.fragment,l)},m(l,c){g(l,d,c),e(d,y),g(l,h,c),w(f,l,c),_=!0},p:Oe,i(l){_||($(f.$$.fragment,l),_=!0)},o(l){F(f.$$.fragment,l),_=!1},d(l){l&&t(d),l&&t(h),P(f,l)}}}function SL(N){let d,y,h,f,_,l,c,x,ye,he,D,ae,te,L,be,Q,ve,ue,A,ke,se,K,Te,ce,V,we,ie,J,$e,fe,ee,R,C,le,H,Fe,ge,U,Pe,_e,z,B,G,de,Ne,X,pe,Le,S,me,Y,xe,oe,q,Me,j,Ee,Re,v,M,Qe,re,ze,He,E,Be,Ke,De,Ve,Je,O,I,Ge,Se,Z,Xe,We,Ce,Ye,ne,qe,Pn,Od,ba,_f,Id,yf,ph,ft,Dd,wl,bf,vf,Sd,$l,kf,Tf,Wd,Fl,wf,$f,Ud,Pl,Ff,Pf,Qd,Nl,Nf,Lf,Hd,Ll,xf,mh,xo,Nn,Bd,va,Mf,Kd,Ef,hh,At,ka,Rf,so,zf,xl,Cf,qf,Ml,jf,Af,Ta,Of,If,Df,Mo,Sf,El,Wf,Uf,Rl,Qf,Hf,Bf,Ln,uh,Eo,xn,Vd,wa,Kf,Jd,Vf,fh,Ze,$a,Jf,Gd,Gf,Xf,Fa,Yf,zl,Zf,eg,tg,Pa,og,Na,ng,rg,ag,La,sg,Xd,ig,lg,dg,it,cg,Yd,pg,mg,Zd,hg,ug,ec,fg,gg,tc,_g,yg,oc,bg,vg,nc,kg,Tg,wg,Mn,$g,rc,Fg,Pg,xa,Ng,Lg,Ot,Ma,xg,Ro,Mg,Cl,Eg,Rg,ac,zg,Cg,qg,En,jg,Rn,gh,zo,zn,sc,Ea,Ag,ic,Og,_h,_t,Ra,Ig,za,Dg,lc,Sg,Wg,Ug,Ca,Qg,ql,Hg,Bg,Kg,qa,Vg,ja,Jg,Gg,Xg,It,Aa,Yg,Co,Zg,jl,e_,t_,dc,o_,n_,r_,Cn,a_,qn,yh,qo,jn,cc,Oa,s_,pc,i_,bh,yt,Ia,l_,Da,d_,mc,c_,p_,m_,Sa,h_,Al,u_,f_,g_,Wa,__,Ua,y_,b_,v_,Dt,Qa,k_,jo,T_,Ol,w_,$_,hc,F_,P_,N_,An,L_,On,vh,Ao,In,uc,Ha,x_,fc,M_,kh,bt,Ba,E_,gc,R_,z_,Ka,C_,Il,q_,j_,A_,Va,O_,Ja,I_,D_,S_,$t,Ga,W_,Oo,U_,Dl,Q_,H_,_c,B_,K_,V_,Dn,J_,Sn,G_,Wn,Th,Io,Un,yc,Xa,X_,bc,Y_,wh,vt,Ya,Z_,vc,ey,ty,Za,oy,Sl,ny,ry,ay,es,sy,ts,iy,ly,dy,St,os,cy,Do,py,Wl,my,hy,kc,uy,fy,gy,Qn,_y,Hn,$h,So,Bn,Tc,ns,yy,wc,by,Fh,kt,rs,vy,$c,ky,Ty,as,wy,Ul,$y,Fy,Py,ss,Ny,is,Ly,xy,My,Wt,ls,Ey,Wo,Ry,Ql,zy,Cy,Fc,qy,jy,Ay,Kn,Oy,Vn,Ph,Uo,Jn,Pc,ds,Iy,Nc,Dy,Nh,Tt,cs,Sy,Qo,Wy,Lc,Uy,Qy,xc,Hy,By,Ky,ps,Vy,Hl,Jy,Gy,Xy,ms,Yy,hs,Zy,eb,tb,Ut,us,ob,Ho,nb,Bl,rb,ab,Mc,sb,ib,lb,Gn,db,Xn,Lh,Bo,Yn,Ec,fs,cb,Rc,pb,xh,lt,gs,mb,zc,hb,ub,_s,fb,Kl,gb,_b,yb,ys,bb,bs,vb,kb,Tb,Zn,wb,Qt,vs,$b,Ko,Fb,Vl,Pb,Nb,Cc,Lb,xb,Mb,er,Eb,tr,Mh,Vo,or,qc,ks,Rb,jc,zb,Eh,Jo,Ts,Cb,Ht,ws,qb,Go,jb,Jl,Ab,Ob,Ac,Ib,Db,Sb,nr,Wb,rr,Rh,Xo,ar,Oc,$s,Ub,Ic,Qb,zh,dt,Fs,Hb,Ps,Bb,Dc,Kb,Vb,Jb,Ns,Gb,Gl,Xb,Yb,Zb,Ls,ev,xs,tv,ov,nv,sr,rv,Ft,Ms,av,Yo,sv,Xl,iv,lv,Sc,dv,cv,pv,ir,mv,lr,hv,dr,Ch,Zo,cr,Wc,Es,uv,Uc,fv,qh,ct,Rs,gv,Qc,_v,yv,zs,bv,Yl,vv,kv,Tv,Cs,wv,qs,$v,Fv,Pv,pr,Nv,Pt,js,Lv,en,xv,Zl,Mv,Ev,Hc,Rv,zv,Cv,mr,qv,hr,jv,ur,jh,tn,fr,Bc,As,Av,Kc,Ov,Ah,pt,Os,Iv,Vc,Dv,Sv,Is,Wv,ed,Uv,Qv,Hv,Ds,Bv,Ss,Kv,Vv,Jv,gr,Gv,Bt,Ws,Xv,on,Yv,td,Zv,ek,Jc,tk,ok,nk,_r,rk,yr,Oh,nn,br,Gc,Us,ak,Xc,sk,Ih,mt,Qs,ik,Yc,lk,dk,Hs,ck,od,pk,mk,hk,Bs,uk,Ks,fk,gk,_k,vr,yk,Nt,Vs,bk,rn,vk,nd,kk,Tk,Zc,wk,$k,Fk,kr,Pk,Tr,Nk,wr,Dh,an,$r,ep,Js,Lk,tp,xk,Sh,ht,Gs,Mk,sn,Ek,op,Rk,zk,np,Ck,qk,jk,Xs,Ak,rd,Ok,Ik,Dk,Ys,Sk,Zs,Wk,Uk,Qk,Fr,Hk,Lt,ei,Bk,ln,Kk,ad,Vk,Jk,rp,Gk,Xk,Yk,Pr,Zk,Nr,eT,Lr,Wh,dn,xr,ap,ti,tT,sp,oT,Uh,et,oi,nT,ip,rT,aT,ni,sT,sd,iT,lT,dT,ri,cT,ai,pT,mT,hT,lp,uT,fT,io,dp,si,gT,_T,cp,ii,yT,bT,pp,li,vT,kT,mp,di,TT,wT,Kt,ci,$T,cn,FT,hp,PT,NT,up,LT,xT,MT,Mr,ET,Er,Qh,pn,Rr,fp,pi,RT,gp,zT,Hh,tt,mi,CT,_p,qT,jT,hi,AT,id,OT,IT,DT,ui,ST,fi,WT,UT,QT,yp,HT,BT,lo,bp,gi,KT,VT,vp,_i,JT,GT,kp,yi,XT,YT,Tp,bi,ZT,ew,Vt,vi,tw,mn,ow,wp,nw,rw,$p,aw,sw,iw,zr,lw,Cr,Bh,hn,qr,Fp,ki,dw,Pp,cw,Kh,ot,Ti,pw,wi,mw,Np,hw,uw,fw,$i,gw,ld,_w,yw,bw,Fi,vw,Pi,kw,Tw,ww,Lp,$w,Fw,co,xp,Ni,Pw,Nw,Mp,Li,Lw,xw,Ep,xi,Mw,Ew,Rp,Mi,Rw,zw,Jt,Ei,Cw,un,qw,zp,jw,Aw,Cp,Ow,Iw,Dw,jr,Sw,Ar,Vh,fn,Or,qp,Ri,Ww,jp,Uw,Jh,nt,zi,Qw,Ap,Hw,Bw,Ci,Kw,dd,Vw,Jw,Gw,qi,Xw,ji,Yw,Zw,e0,Op,t0,o0,po,Ip,Ai,n0,r0,Dp,Oi,a0,s0,Sp,Ii,i0,l0,Wp,Di,d0,c0,Gt,Si,p0,gn,m0,Up,h0,u0,Qp,f0,g0,_0,Ir,y0,Dr,Gh,_n,Sr,Hp,Wi,b0,Bp,v0,Xh,rt,Ui,k0,Kp,T0,w0,Qi,$0,cd,F0,P0,N0,Hi,L0,Bi,x0,M0,E0,Vp,R0,z0,mo,Jp,Ki,C0,q0,Gp,Vi,j0,A0,Xp,Ji,O0,I0,Yp,Gi,D0,S0,Xt,Xi,W0,yn,U0,Zp,Q0,H0,em,B0,K0,V0,Wr,J0,Ur,Yh,bn,Qr,tm,Yi,G0,om,X0,Zh,at,Zi,Y0,nm,Z0,e$,el,t$,pd,o$,n$,r$,tl,a$,ol,s$,i$,l$,rm,d$,c$,ho,am,nl,p$,m$,sm,rl,h$,u$,im,al,f$,g$,lm,sl,_$,y$,Yt,il,b$,vn,v$,dm,k$,T$,cm,w$,$$,F$,Hr,P$,Br,eu,kn,Kr,pm,ll,N$,mm,L$,tu,st,dl,x$,Tn,M$,hm,E$,R$,um,z$,C$,q$,cl,j$,md,A$,O$,I$,pl,D$,ml,S$,W$,U$,fm,Q$,H$,uo,gm,hl,B$,K$,_m,ul,V$,J$,ym,fl,G$,X$,bm,gl,Y$,Z$,Zt,_l,e2,wn,t2,vm,o2,n2,km,r2,a2,s2,Vr,i2,Jr,ou;return l=new Ue({}),L=new Ue({}),ba=new Ue({}),va=new Ue({}),ka=new W({props:{name:"class transformers.RobertaPreLayerNormConfig",anchor:"transformers.RobertaPreLayerNormConfig",parameters:[{name:"vocab_size",val:" = 30522"},{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.1"},{name:"attention_probs_dropout_prob",val:" = 0.1"},{name:"max_position_embeddings",val:" = 512"},{name:"type_vocab_size",val:" = 2"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"pad_token_id",val:" = 1"},{name:"bos_token_id",val:" = 0"},{name:"eos_token_id",val:" = 2"},{name:"position_embedding_type",val:" = 'absolute'"},{name:"use_cache",val:" = True"},{name:"classifier_dropout",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
Vocabulary size of the RoBERTa-PreLayerNorm model. Defines the number of different tokens that can be
represented by the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormModel">RobertaPreLayerNormModel</a> or
<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormModel">TFRobertaPreLayerNormModel</a>.`,name:"vocab_size"},{anchor:"transformers.RobertaPreLayerNormConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.RobertaPreLayerNormConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.RobertaPreLayerNormConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.RobertaPreLayerNormConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.RobertaPreLayerNormConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>Callable</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.RobertaPreLayerNormConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.RobertaPreLayerNormConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.RobertaPreLayerNormConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.RobertaPreLayerNormConfig.type_vocab_size",description:`<strong>type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The vocabulary size of the <code>token_type_ids</code> passed when calling <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormModel">RobertaPreLayerNormModel</a> or
<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormModel">TFRobertaPreLayerNormModel</a>.`,name:"type_vocab_size"},{anchor:"transformers.RobertaPreLayerNormConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.RobertaPreLayerNormConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.RobertaPreLayerNormConfig.position_embedding_type",description:`<strong>position_embedding_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;absolute&quot;</code>) &#x2014;
Type of position embedding. Choose one of <code>&quot;absolute&quot;</code>, <code>&quot;relative_key&quot;</code>, <code>&quot;relative_key_query&quot;</code>. For
positional embeddings use <code>&quot;absolute&quot;</code>. For more information on <code>&quot;relative_key&quot;</code>, please refer to
<a href="https://arxiv.org/abs/1803.02155" rel="nofollow">Self-Attention with Relative Position Representations (Shaw et al.)</a>.
For more information on <code>&quot;relative_key_query&quot;</code>, please refer to <em>Method 4</em> in <a href="https://arxiv.org/abs/2009.13658" rel="nofollow">Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)</a>.`,name:"position_embedding_type"},{anchor:"transformers.RobertaPreLayerNormConfig.is_decoder",description:`<strong>is_decoder</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether the model is used as a decoder or not. If <code>False</code>, the model is used as an encoder.`,name:"is_decoder"},{anchor:"transformers.RobertaPreLayerNormConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if <code>config.is_decoder=True</code>.`,name:"use_cache"},{anchor:"transformers.RobertaPreLayerNormConfig.classifier_dropout",description:`<strong>classifier_dropout</strong> (<code>float</code>, <em>optional</em>) &#x2014;
The dropout ratio for the classification head.`,name:"classifier_dropout"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/configuration_roberta_prelayernorm.py#L35"}}),Ln=new Ae({props:{anchor:"transformers.RobertaPreLayerNormConfig.example",$$slots:{default:[SN]},$$scope:{ctx:N}}}),wa=new Ue({}),$a=new W({props:{name:"class transformers.RobertaPreLayerNormModel",anchor:"transformers.RobertaPreLayerNormModel",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = True"}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py#L701"}}),Ma=new W({props:{name:"forward",anchor:"transformers.RobertaPreLayerNormModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.List[torch.FloatTensor]] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.RobertaPreLayerNormModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.RobertaPreLayerNormModel.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0,1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
This parameter can only be used when the model is initialized with <code>type_vocab_size</code> parameter with value<blockquote>
<p>= 2. All the value in this tensor should be always &lt; type_vocab_size.</p>
</blockquote></li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RobertaPreLayerNormModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RobertaPreLayerNormModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.RobertaPreLayerNormModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RobertaPreLayerNormModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.RobertaPreLayerNormModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.RobertaPreLayerNormModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RobertaPreLayerNormModel.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.RobertaPreLayerNormModel.forward.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"encoder_attention_mask"},{anchor:"transformers.RobertaPreLayerNormModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.RobertaPreLayerNormModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py#L746",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> and <code>config.add_cross_attention=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and optionally if
<code>config.is_encoder_decoder=True</code> 2 additional tensors of shape <code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
<code>config.is_encoder_decoder=True</code> in the cross-attention blocks) that can be used (see <code>past_key_values</code>
input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),En=new je({props:{$$slots:{default:[WN]},$$scope:{ctx:N}}}),Rn=new Ae({props:{anchor:"transformers.RobertaPreLayerNormModel.forward.example",$$slots:{default:[UN]},$$scope:{ctx:N}}}),Ea=new Ue({}),Ra=new W({props:{name:"class transformers.RobertaPreLayerNormForCausalLM",anchor:"transformers.RobertaPreLayerNormForCausalLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py#L888"}}),Aa=new W({props:{name:"forward",anchor:"transformers.RobertaPreLayerNormForCausalLM.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"past_key_values",val:": typing.Tuple[typing.Tuple[torch.FloatTensor]] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0,1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
This parameter can only be used when the model is initialized with <code>type_vocab_size</code> parameter with value<blockquote>
<p>= 2. All the value in this tensor should be always &lt; type_vocab_size.</p>
</blockquote></li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"encoder_attention_mask"},{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
<code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are
ignored (masked), the loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"},{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py#L916",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Language modeling loss (for next-token prediction).</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Cn=new je({props:{$$slots:{default:[QN]},$$scope:{ctx:N}}}),qn=new Ae({props:{anchor:"transformers.RobertaPreLayerNormForCausalLM.forward.example",$$slots:{default:[HN]},$$scope:{ctx:N}}}),Oa=new Ue({}),Ia=new W({props:{name:"class transformers.RobertaPreLayerNormForMaskedLM",anchor:"transformers.RobertaPreLayerNormForMaskedLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py#L1046"}}),Qa=new W({props:{name:"forward",anchor:"transformers.RobertaPreLayerNormForMaskedLM.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormForMaskedLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.RobertaPreLayerNormForMaskedLM.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.RobertaPreLayerNormForMaskedLM.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0,1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
This parameter can only be used when the model is initialized with <code>type_vocab_size</code> parameter with value<blockquote>
<p>= 2. All the value in this tensor should be always &lt; type_vocab_size.</p>
</blockquote></li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RobertaPreLayerNormForMaskedLM.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RobertaPreLayerNormForMaskedLM.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.RobertaPreLayerNormForMaskedLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RobertaPreLayerNormForMaskedLM.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.RobertaPreLayerNormForMaskedLM.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.RobertaPreLayerNormForMaskedLM.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RobertaPreLayerNormForMaskedLM.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are ignored (masked), the
loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"},{anchor:"transformers.RobertaPreLayerNormForMaskedLM.forward.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, any]</code>, optional, defaults to <em>{}</em>) &#x2014;
Used to hide legacy arguments that have been deprecated.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py#L1076",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Masked language modeling (MLM) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),An=new je({props:{$$slots:{default:[BN]},$$scope:{ctx:N}}}),On=new Ae({props:{anchor:"transformers.RobertaPreLayerNormForMaskedLM.forward.example",$$slots:{default:[KN]},$$scope:{ctx:N}}}),Ha=new Ue({}),Ba=new W({props:{name:"class transformers.RobertaPreLayerNormForSequenceClassification",anchor:"transformers.RobertaPreLayerNormForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py#L1185"}}),Ga=new W({props:{name:"forward",anchor:"transformers.RobertaPreLayerNormForSequenceClassification.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormForSequenceClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.RobertaPreLayerNormForSequenceClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.RobertaPreLayerNormForSequenceClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0,1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
This parameter can only be used when the model is initialized with <code>type_vocab_size</code> parameter with value<blockquote>
<p>= 2. All the value in this tensor should be always &lt; type_vocab_size.</p>
</blockquote></li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RobertaPreLayerNormForSequenceClassification.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RobertaPreLayerNormForSequenceClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.RobertaPreLayerNormForSequenceClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RobertaPreLayerNormForSequenceClassification.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.RobertaPreLayerNormForSequenceClassification.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.RobertaPreLayerNormForSequenceClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RobertaPreLayerNormForSequenceClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py#L1199",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Dn=new je({props:{$$slots:{default:[VN]},$$scope:{ctx:N}}}),Sn=new Ae({props:{anchor:"transformers.RobertaPreLayerNormForSequenceClassification.forward.example",$$slots:{default:[JN]},$$scope:{ctx:N}}}),Wn=new Ae({props:{anchor:"transformers.RobertaPreLayerNormForSequenceClassification.forward.example-2",$$slots:{default:[GN]},$$scope:{ctx:N}}}),Xa=new Ue({}),Ya=new W({props:{name:"class transformers.RobertaPreLayerNormForMultipleChoice",anchor:"transformers.RobertaPreLayerNormForMultipleChoice",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py#L1286"}}),os=new W({props:{name:"forward",anchor:"transformers.RobertaPreLayerNormForMultipleChoice.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormForMultipleChoice.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.RobertaPreLayerNormForMultipleChoice.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.RobertaPreLayerNormForMultipleChoice.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0,1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
This parameter can only be used when the model is initialized with <code>type_vocab_size</code> parameter with value<blockquote>
<p>= 2. All the value in this tensor should be always &lt; type_vocab_size.</p>
</blockquote></li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RobertaPreLayerNormForMultipleChoice.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RobertaPreLayerNormForMultipleChoice.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.RobertaPreLayerNormForMultipleChoice.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RobertaPreLayerNormForMultipleChoice.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.RobertaPreLayerNormForMultipleChoice.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.RobertaPreLayerNormForMultipleChoice.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RobertaPreLayerNormForMultipleChoice.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the multiple choice classification loss. Indices should be in <code>[0, ..., num_choices-1]</code> where <code>num_choices</code> is the size of the second dimension of the input tensors. (See
<code>input_ids</code> above)`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py#L1299",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <em>(1,)</em>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices)</code>) \u2014 <em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Qn=new je({props:{$$slots:{default:[XN]},$$scope:{ctx:N}}}),Hn=new Ae({props:{anchor:"transformers.RobertaPreLayerNormForMultipleChoice.forward.example",$$slots:{default:[YN]},$$scope:{ctx:N}}}),ns=new Ue({}),rs=new W({props:{name:"class transformers.RobertaPreLayerNormForTokenClassification",anchor:"transformers.RobertaPreLayerNormForTokenClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py#L1382"}}),ls=new W({props:{name:"forward",anchor:"transformers.RobertaPreLayerNormForTokenClassification.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormForTokenClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.RobertaPreLayerNormForTokenClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.RobertaPreLayerNormForTokenClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0,1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
This parameter can only be used when the model is initialized with <code>type_vocab_size</code> parameter with value<blockquote>
<p>= 2. All the value in this tensor should be always &lt; type_vocab_size.</p>
</blockquote></li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RobertaPreLayerNormForTokenClassification.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RobertaPreLayerNormForTokenClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.RobertaPreLayerNormForTokenClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RobertaPreLayerNormForTokenClassification.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.RobertaPreLayerNormForTokenClassification.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.RobertaPreLayerNormForTokenClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RobertaPreLayerNormForTokenClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the token classification loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py#L1400",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided)  \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) \u2014 Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Kn=new je({props:{$$slots:{default:[ZN]},$$scope:{ctx:N}}}),Vn=new Ae({props:{anchor:"transformers.RobertaPreLayerNormForTokenClassification.forward.example",$$slots:{default:[eL]},$$scope:{ctx:N}}}),ds=new Ue({}),cs=new W({props:{name:"class transformers.RobertaPreLayerNormForQuestionAnswering",anchor:"transformers.RobertaPreLayerNormForQuestionAnswering",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py#L1492"}}),us=new W({props:{name:"forward",anchor:"transformers.RobertaPreLayerNormForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"start_positions",val:": typing.Optional[torch.LongTensor] = None"},{name:"end_positions",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.RobertaPreLayerNormForQuestionAnswering.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.RobertaPreLayerNormForQuestionAnswering.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.RobertaPreLayerNormForQuestionAnswering.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0,1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
This parameter can only be used when the model is initialized with <code>type_vocab_size</code> parameter with value<blockquote>
<p>= 2. All the value in this tensor should be always &lt; type_vocab_size.</p>
</blockquote></li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RobertaPreLayerNormForQuestionAnswering.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RobertaPreLayerNormForQuestionAnswering.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.RobertaPreLayerNormForQuestionAnswering.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RobertaPreLayerNormForQuestionAnswering.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.RobertaPreLayerNormForQuestionAnswering.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.RobertaPreLayerNormForQuestionAnswering.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RobertaPreLayerNormForQuestionAnswering.forward.start_positions",description:`<strong>start_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"start_positions"},{anchor:"transformers.RobertaPreLayerNormForQuestionAnswering.forward.end_positions",description:`<strong>end_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"end_positions"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py#L1506",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput"
>transformers.modeling_outputs.QuestionAnsweringModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.</p>
</li>
<li>
<p><strong>start_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-start scores (before SoftMax).</p>
</li>
<li>
<p><strong>end_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-end scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput"
>transformers.modeling_outputs.QuestionAnsweringModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Gn=new je({props:{$$slots:{default:[tL]},$$scope:{ctx:N}}}),Xn=new Ae({props:{anchor:"transformers.RobertaPreLayerNormForQuestionAnswering.forward.example",$$slots:{default:[oL]},$$scope:{ctx:N}}}),fs=new Ue({}),gs=new W({props:{name:"class transformers.TFRobertaPreLayerNormModel",anchor:"transformers.TFRobertaPreLayerNormModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRobertaPreLayerNormModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py#L923"}}),Zn=new je({props:{$$slots:{default:[nL]},$$scope:{ctx:N}}}),vs=new W({props:{name:"call",anchor:"transformers.TFRobertaPreLayerNormModel.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"inputs_embeds",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"encoder_hidden_states",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"encoder_attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"past_key_values",val:": typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"training",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFRobertaPreLayerNormModel.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRobertaPreLayerNormModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFRobertaPreLayerNormModel.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRobertaPreLayerNormModel.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRobertaPreLayerNormModel.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFRobertaPreLayerNormModel.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFRobertaPreLayerNormModel.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFRobertaPreLayerNormModel.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFRobertaPreLayerNormModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFRobertaPreLayerNormModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFRobertaPreLayerNormModel.call.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.TFRobertaPreLayerNormModel.call.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"encoder_attention_mask"},{anchor:"transformers.TFRobertaPreLayerNormModel.call.past_key_values",description:`<strong>past_key_values</strong> (<code>Tuple[Tuple[tf.Tensor]]</code> of length <code>config.n_layers</code>) &#x2014;
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.TFRobertaPreLayerNormModel.call.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>). Set to <code>False</code> during training, <code>True</code> during generation`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py#L928",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
<p>This output is usually <em>not</em> a good summary of the semantic content of the input, you\u2019re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions</a> or <code>tuple(tf.Tensor)</code></p>
`}}),er=new je({props:{$$slots:{default:[rL]},$$scope:{ctx:N}}}),tr=new Ae({props:{anchor:"transformers.TFRobertaPreLayerNormModel.call.example",$$slots:{default:[aL]},$$scope:{ctx:N}}}),ks=new Ue({}),Ts=new W({props:{name:"class transformers.TFRobertaPreLayerNormForCausalLM",anchor:"transformers.TFRobertaPreLayerNormForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py#L1158"}}),ws=new W({props:{name:"call",anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"inputs_embeds",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"encoder_hidden_states",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"encoder_attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"past_key_values",val:": typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"encoder_attention_mask"},{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.past_key_values",description:`<strong>past_key_values</strong> (<code>Tuple[Tuple[tf.Tensor]]</code> of length <code>config.n_layers</code>) &#x2014;
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>). Set to <code>False</code> during training, <code>True</code> during generation`,name:"use_cache"},{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> or <code>np.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the cross entropy classification loss. Indices should be in <code>[0, ..., config.vocab_size - 1]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py#L1197",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions"
>transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of non-masked labels, returned when <code>labels</code> is provided) \u2014 Language modeling loss (for next-token prediction).</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions"
>transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions</a> or <code>tuple(tf.Tensor)</code></p>
`}}),nr=new je({props:{$$slots:{default:[sL]},$$scope:{ctx:N}}}),rr=new Ae({props:{anchor:"transformers.TFRobertaPreLayerNormForCausalLM.call.example",$$slots:{default:[iL]},$$scope:{ctx:N}}}),$s=new Ue({}),Fs=new W({props:{name:"class transformers.TFRobertaPreLayerNormForMaskedLM",anchor:"transformers.TFRobertaPreLayerNormForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py#L1069"}}),sr=new je({props:{$$slots:{default:[lL]},$$scope:{ctx:N}}}),Ms=new W({props:{name:"call",anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"inputs_embeds",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are ignored (masked), the
loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py#L1089",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput"
>transformers.modeling_tf_outputs.TFMaskedLMOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of non-masked labels, returned when <code>labels</code> is provided) \u2014 Masked language modeling (MLM) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput"
>transformers.modeling_tf_outputs.TFMaskedLMOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),ir=new je({props:{$$slots:{default:[dL]},$$scope:{ctx:N}}}),lr=new Ae({props:{anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.call.example",$$slots:{default:[cL]},$$scope:{ctx:N}}}),dr=new Ae({props:{anchor:"transformers.TFRobertaPreLayerNormForMaskedLM.call.example-2",$$slots:{default:[pL]},$$scope:{ctx:N}}}),Es=new Ue({}),Rs=new W({props:{name:"class transformers.TFRobertaPreLayerNormForSequenceClassification",anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py#L1336"}}),pr=new je({props:{$$slots:{default:[mL]},$$scope:{ctx:N}}}),js=new W({props:{name:"call",anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"inputs_embeds",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py#L1351",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, )</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),mr=new je({props:{$$slots:{default:[hL]},$$scope:{ctx:N}}}),hr=new Ae({props:{anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.call.example",$$slots:{default:[uL]},$$scope:{ctx:N}}}),ur=new Ae({props:{anchor:"transformers.TFRobertaPreLayerNormForSequenceClassification.call.example-2",$$slots:{default:[fL]},$$scope:{ctx:N}}}),As=new Ue({}),Os=new W({props:{name:"class transformers.TFRobertaPreLayerNormForMultipleChoice",anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py#L1423"}}),gr=new je({props:{$$slots:{default:[gL]},$$scope:{ctx:N}}}),Ws=new W({props:{name:"call",anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"inputs_embeds",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the multiple choice classification loss. Indices should be in <code>[0, ..., num_choices]</code>
where <code>num_choices</code> is the size of the second dimension of the input tensors. (See <code>input_ids</code> above)`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py#L1447",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput"
>transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <em>(batch_size, )</em>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_choices)</code>) \u2014 <em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput"
>transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),_r=new je({props:{$$slots:{default:[_L]},$$scope:{ctx:N}}}),yr=new Ae({props:{anchor:"transformers.TFRobertaPreLayerNormForMultipleChoice.call.example",$$slots:{default:[yL]},$$scope:{ctx:N}}}),Us=new Ue({}),Qs=new W({props:{name:"class transformers.TFRobertaPreLayerNormForTokenClassification",anchor:"transformers.TFRobertaPreLayerNormForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py#L1545"}}),vr=new je({props:{$$slots:{default:[bL]},$$scope:{ctx:N}}}),Vs=new W({props:{name:"call",anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"inputs_embeds",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the token classification loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py#L1565",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput"
>transformers.modeling_tf_outputs.TFTokenClassifierOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of unmasked labels, returned when <code>labels</code> is provided)  \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) \u2014 Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput"
>transformers.modeling_tf_outputs.TFTokenClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),kr=new je({props:{$$slots:{default:[vL]},$$scope:{ctx:N}}}),Tr=new Ae({props:{anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.call.example",$$slots:{default:[kL]},$$scope:{ctx:N}}}),wr=new Ae({props:{anchor:"transformers.TFRobertaPreLayerNormForTokenClassification.call.example-2",$$slots:{default:[TL]},$$scope:{ctx:N}}}),Js=new Ue({}),Gs=new W({props:{name:"class transformers.TFRobertaPreLayerNormForQuestionAnswering",anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py#L1636"}}),Fr=new je({props:{$$slots:{default:[wL]},$$scope:{ctx:N}}}),ei=new W({props:{name:"call",anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"inputs_embeds",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"start_positions",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"end_positions",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call.start_positions",description:`<strong>start_positions</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"start_positions"},{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call.end_positions",description:`<strong>end_positions</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"end_positions"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py#L1651",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput"
>transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, )</code>, <em>optional</em>, returned when <code>start_positions</code> and <code>end_positions</code> are provided) \u2014 Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.</p>
</li>
<li>
<p><strong>start_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-start scores (before SoftMax).</p>
</li>
<li>
<p><strong>end_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-end scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput"
>transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Pr=new je({props:{$$slots:{default:[$L]},$$scope:{ctx:N}}}),Nr=new Ae({props:{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call.example",$$slots:{default:[FL]},$$scope:{ctx:N}}}),Lr=new Ae({props:{anchor:"transformers.TFRobertaPreLayerNormForQuestionAnswering.call.example-2",$$slots:{default:[PL]},$$scope:{ctx:N}}}),ti=new Ue({}),oi=new W({props:{name:"class transformers.FlaxRobertaPreLayerNormModel",anchor:"transformers.FlaxRobertaPreLayerNormModel",parameters:[{name:"config",val:": RobertaPreLayerNormConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxRobertaPreLayerNormModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py#L995"}}),ci=new W({props:{name:"__call__",anchor:"transformers.FlaxRobertaPreLayerNormModel.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxRobertaPreLayerNormModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxRobertaPreLayerNormModel.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormModel.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormModel.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxRobertaPreLayerNormModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py#L819",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Mr=new je({props:{$$slots:{default:[NL]},$$scope:{ctx:N}}}),Er=new Ae({props:{anchor:"transformers.FlaxRobertaPreLayerNormModel.__call__.example",$$slots:{default:[LL]},$$scope:{ctx:N}}}),pi=new Ue({}),mi=new W({props:{name:"class transformers.FlaxRobertaPreLayerNormForCausalLM",anchor:"transformers.FlaxRobertaPreLayerNormForCausalLM",parameters:[{name:"config",val:": RobertaPreLayerNormConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxRobertaPreLayerNormForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py#L1478"}}),vi=new W({props:{name:"__call__",anchor:"transformers.FlaxRobertaPreLayerNormForCausalLM.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxRobertaPreLayerNormForCausalLM.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForCausalLM.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxRobertaPreLayerNormForCausalLM.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForCausalLM.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForCausalLM.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxRobertaPreLayerNormForCausalLM.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py#L819",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions"
>transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key, value
states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting.
Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions"
>transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),zr=new je({props:{$$slots:{default:[xL]},$$scope:{ctx:N}}}),Cr=new Ae({props:{anchor:"transformers.FlaxRobertaPreLayerNormForCausalLM.__call__.example",$$slots:{default:[ML]},$$scope:{ctx:N}}}),ki=new Ue({}),Ti=new W({props:{name:"class transformers.FlaxRobertaPreLayerNormForMaskedLM",anchor:"transformers.FlaxRobertaPreLayerNormForMaskedLM",parameters:[{name:"config",val:": RobertaPreLayerNormConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxRobertaPreLayerNormForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py#L1072"}}),Ei=new W({props:{name:"__call__",anchor:"transformers.FlaxRobertaPreLayerNormForMaskedLM.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxRobertaPreLayerNormForMaskedLM.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForMaskedLM.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxRobertaPreLayerNormForMaskedLM.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForMaskedLM.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForMaskedLM.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxRobertaPreLayerNormForMaskedLM.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py#L819",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),jr=new je({props:{$$slots:{default:[EL]},$$scope:{ctx:N}}}),Ar=new Ae({props:{anchor:"transformers.FlaxRobertaPreLayerNormForMaskedLM.__call__.example",$$slots:{default:[RL]},$$scope:{ctx:N}}}),Ri=new Ue({}),zi=new W({props:{name:"class transformers.FlaxRobertaPreLayerNormForSequenceClassification",anchor:"transformers.FlaxRobertaPreLayerNormForSequenceClassification",parameters:[{name:"config",val:": RobertaPreLayerNormConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxRobertaPreLayerNormForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py#L1146"}}),Si=new W({props:{name:"__call__",anchor:"transformers.FlaxRobertaPreLayerNormForSequenceClassification.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxRobertaPreLayerNormForSequenceClassification.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForSequenceClassification.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxRobertaPreLayerNormForSequenceClassification.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForSequenceClassification.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForSequenceClassification.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxRobertaPreLayerNormForSequenceClassification.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py#L819",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"
>transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"
>transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ir=new je({props:{$$slots:{default:[zL]},$$scope:{ctx:N}}}),Dr=new Ae({props:{anchor:"transformers.FlaxRobertaPreLayerNormForSequenceClassification.__call__.example",$$slots:{default:[CL]},$$scope:{ctx:N}}}),Wi=new Ue({}),Ui=new W({props:{name:"class transformers.FlaxRobertaPreLayerNormForMultipleChoice",anchor:"transformers.FlaxRobertaPreLayerNormForMultipleChoice",parameters:[{name:"config",val:": RobertaPreLayerNormConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxRobertaPreLayerNormForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py#L1228"}}),Xi=new W({props:{name:"__call__",anchor:"transformers.FlaxRobertaPreLayerNormForMultipleChoice.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxRobertaPreLayerNormForMultipleChoice.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForMultipleChoice.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxRobertaPreLayerNormForMultipleChoice.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForMultipleChoice.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForMultipleChoice.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxRobertaPreLayerNormForMultipleChoice.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py#L819",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"
>transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, num_choices)</code>) \u2014 <em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"
>transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Wr=new je({props:{$$slots:{default:[qL]},$$scope:{ctx:N}}}),Ur=new Ae({props:{anchor:"transformers.FlaxRobertaPreLayerNormForMultipleChoice.__call__.example",$$slots:{default:[jL]},$$scope:{ctx:N}}}),Yi=new Ue({}),Zi=new W({props:{name:"class transformers.FlaxRobertaPreLayerNormForTokenClassification",anchor:"transformers.FlaxRobertaPreLayerNormForTokenClassification",parameters:[{name:"config",val:": RobertaPreLayerNormConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxRobertaPreLayerNormForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py#L1312"}}),il=new W({props:{name:"__call__",anchor:"transformers.FlaxRobertaPreLayerNormForTokenClassification.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxRobertaPreLayerNormForTokenClassification.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForTokenClassification.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxRobertaPreLayerNormForTokenClassification.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForTokenClassification.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForTokenClassification.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxRobertaPreLayerNormForTokenClassification.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py#L819",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"
>transformers.modeling_flax_outputs.FlaxTokenClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) \u2014 Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"
>transformers.modeling_flax_outputs.FlaxTokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Hr=new je({props:{$$slots:{default:[AL]},$$scope:{ctx:N}}}),Br=new Ae({props:{anchor:"transformers.FlaxRobertaPreLayerNormForTokenClassification.__call__.example",$$slots:{default:[OL]},$$scope:{ctx:N}}}),ll=new Ue({}),dl=new W({props:{name:"class transformers.FlaxRobertaPreLayerNormForQuestionAnswering",anchor:"transformers.FlaxRobertaPreLayerNormForQuestionAnswering",parameters:[{name:"config",val:": RobertaPreLayerNormConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxRobertaPreLayerNormForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a>) &#x2014; Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py#L1390"}}),_l=new W({props:{name:"__call__",anchor:"transformers.FlaxRobertaPreLayerNormForQuestionAnswering.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxRobertaPreLayerNormForQuestionAnswering.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForQuestionAnswering.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxRobertaPreLayerNormForQuestionAnswering.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForQuestionAnswering.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxRobertaPreLayerNormForQuestionAnswering.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxRobertaPreLayerNormForQuestionAnswering.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py#L819",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput"
>transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig"
>RobertaPreLayerNormConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>start_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-start scores (before SoftMax).</p>
</li>
<li>
<p><strong>end_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-end scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput"
>transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
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etc.)`),Kg=p(),qa=r("p"),Vg=o("This model is also a PyTorch "),ja=r("a"),Jg=o("torch.nn.Module"),Gg=o(` subclass.
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etc.)`),g_=p(),Wa=r("p"),__=o("This model is also a PyTorch "),Ua=r("a"),y_=o("torch.nn.Module"),b_=o(` subclass.
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),A_=p(),Va=r("p"),O_=o("This model is also a PyTorch "),Ja=r("a"),I_=o("torch.nn.Module"),D_=o(` subclass.
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etc.)`),ay=p(),es=r("p"),sy=o("This model is also a PyTorch "),ts=r("a"),iy=o("torch.nn.Module"),ly=o(` subclass.
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etc.)`),Py=p(),ss=r("p"),Ny=o("This model is also a PyTorch "),is=r("a"),Ly=o("torch.nn.Module"),xy=o(` subclass.
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Xy=p(),ms=r("p"),Yy=o("This model is also a PyTorch "),hs=r("a"),Zy=o("torch.nn.Module"),eb=o(` subclass.
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etc.)`),yb=p(),ys=r("p"),bb=o("This model is also a "),bs=r("a"),vb=o("tf.keras.Model"),kb=o(` subclass. Use it
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etc.)`),Zb=p(),Ls=r("p"),ev=o("This model is also a "),xs=r("a"),tv=o("tf.keras.Model"),ov=o(` subclass. Use it
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Tv=p(),Cs=r("p"),wv=o("This model is also a "),qs=r("a"),$v=o("tf.keras.Model"),Fv=o(` subclass. Use it
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Hv=p(),Ds=r("p"),Bv=o("This model is also a "),Ss=r("a"),Kv=o("tf.keras.Model"),Vv=o(` subclass. Use it
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),hk=p(),Bs=r("p"),uk=o("This model is also a "),Ks=r("a"),fk=o("tf.keras.Model"),gk=o(` subclass. Use it
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Dk=p(),Ys=r("p"),Sk=o("This model is also a "),Zs=r("a"),Wk=o("tf.keras.Model"),Uk=o(` subclass. Use it
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
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etc.)`),hu.forEach(t),g_=m(bo),Wa=a(bo,"P",{});var uu=s(Wa);__=n(uu,"This model is also a PyTorch "),Ua=a(uu,"A",{href:!0,rel:!0});var W2=s(Ua);y_=n(W2,"torch.nn.Module"),W2.forEach(t),b_=n(uu,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),uu.forEach(t),v_=m(bo),Dt=a(bo,"DIV",{class:!0});var Yr=s(Dt);T(Qa.$$.fragment,Yr),k_=m(Yr),jo=a(Yr,"P",{});var gd=s(jo);T_=n(gd,"The "),Ol=a(gd,"A",{href:!0});var U2=s(Ol);w_=n(U2,"RobertaPreLayerNormForMaskedLM"),U2.forEach(t),$_=n(gd," forward method, overrides the "),hc=a(gd,"CODE",{});var Q2=s(hc);F_=n(Q2,"__call__"),Q2.forEach(t),P_=n(gd," special method."),gd.forEach(t),N_=m(Yr),T(An.$$.fragment,Yr),L_=m(Yr),T(On.$$.fragment,Yr),Yr.forEach(t),bo.forEach(t),vh=m(i),Ao=a(i,"H2",{class:!0});var fu=s(Ao);In=a(fu,"A",{id:!0,class:!0,href:!0});var H2=s(In);uc=a(H2,"SPAN",{});var B2=s(uc);T(Ha.$$.fragment,B2),B2.forEach(t),H2.forEach(t),x_=m(fu),fc=a(fu,"SPAN",{});var K2=s(fc);M_=n(K2,"RobertaPreLayerNormForSequenceClassification"),K2.forEach(t),fu.forEach(t),kh=m(i),bt=a(i,"DIV",{class:!0});var vo=s(bt);T(Ba.$$.fragment,vo),E_=m(vo),gc=a(vo,"P",{});var V2=s(gc);R_=n(V2,`RoBERTa-PreLayerNorm Model transformer with a sequence classification/regression head on top (a linear layer on top
of the pooled output) e.g. for GLUE tasks.`),V2.forEach(t),z_=m(vo),Ka=a(vo,"P",{});var gu=s(Ka);C_=n(gu,"This model inherits from "),Il=a(gu,"A",{href:!0});var J2=s(Il);q_=n(J2,"PreTrainedModel"),J2.forEach(t),j_=n(gu,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),gu.forEach(t),A_=m(vo),Va=a(vo,"P",{});var _u=s(Va);O_=n(_u,"This model is also a PyTorch "),Ja=a(_u,"A",{href:!0,rel:!0});var G2=s(Ja);I_=n(G2,"torch.nn.Module"),G2.forEach(t),D_=n(_u,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),_u.forEach(t),S_=m(vo),$t=a(vo,"DIV",{class:!0});var ko=s($t);T(Ga.$$.fragment,ko),W_=m(ko),Oo=a(ko,"P",{});var _d=s(Oo);U_=n(_d,"The "),Dl=a(_d,"A",{href:!0});var X2=s(Dl);Q_=n(X2,"RobertaPreLayerNormForSequenceClassification"),X2.forEach(t),H_=n(_d," forward method, overrides the "),_c=a(_d,"CODE",{});var Y2=s(_c);B_=n(Y2,"__call__"),Y2.forEach(t),K_=n(_d," special method."),_d.forEach(t),V_=m(ko),T(Dn.$$.fragment,ko),J_=m(ko),T(Sn.$$.fragment,ko),G_=m(ko),T(Wn.$$.fragment,ko),ko.forEach(t),vo.forEach(t),Th=m(i),Io=a(i,"H2",{class:!0});var yu=s(Io);Un=a(yu,"A",{id:!0,class:!0,href:!0});var Z2=s(Un);yc=a(Z2,"SPAN",{});var e1=s(yc);T(Xa.$$.fragment,e1),e1.forEach(t),Z2.forEach(t),X_=m(yu),bc=a(yu,"SPAN",{});var t1=s(bc);Y_=n(t1,"RobertaPreLayerNormForMultipleChoice"),t1.forEach(t),yu.forEach(t),wh=m(i),vt=a(i,"DIV",{class:!0});var To=s(vt);T(Ya.$$.fragment,To),Z_=m(To),vc=a(To,"P",{});var o1=s(vc);ey=n(o1,`RobertaPreLayerNorm Model with a multiple choice classification head on top (a linear layer on top of the pooled
output and a softmax) e.g. for RocStories/SWAG tasks.`),o1.forEach(t),ty=m(To),Za=a(To,"P",{});var bu=s(Za);oy=n(bu,"This model inherits from "),Sl=a(bu,"A",{href:!0});var n1=s(Sl);ny=n(n1,"PreTrainedModel"),n1.forEach(t),ry=n(bu,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),bu.forEach(t),ay=m(To),es=a(To,"P",{});var vu=s(es);sy=n(vu,"This model is also a PyTorch "),ts=a(vu,"A",{href:!0,rel:!0});var r1=s(ts);iy=n(r1,"torch.nn.Module"),r1.forEach(t),ly=n(vu,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),vu.forEach(t),dy=m(To),St=a(To,"DIV",{class:!0});var Zr=s(St);T(os.$$.fragment,Zr),cy=m(Zr),Do=a(Zr,"P",{});var yd=s(Do);py=n(yd,"The "),Wl=a(yd,"A",{href:!0});var a1=s(Wl);my=n(a1,"RobertaPreLayerNormForMultipleChoice"),a1.forEach(t),hy=n(yd," forward method, overrides the "),kc=a(yd,"CODE",{});var s1=s(kc);uy=n(s1,"__call__"),s1.forEach(t),fy=n(yd," special method."),yd.forEach(t),gy=m(Zr),T(Qn.$$.fragment,Zr),_y=m(Zr),T(Hn.$$.fragment,Zr),Zr.forEach(t),To.forEach(t),$h=m(i),So=a(i,"H2",{class:!0});var ku=s(So);Bn=a(ku,"A",{id:!0,class:!0,href:!0});var i1=s(Bn);Tc=a(i1,"SPAN",{});var l1=s(Tc);T(ns.$$.fragment,l1),l1.forEach(t),i1.forEach(t),yy=m(ku),wc=a(ku,"SPAN",{});var d1=s(wc);by=n(d1,"RobertaPreLayerNormForTokenClassification"),d1.forEach(t),ku.forEach(t),Fh=m(i),kt=a(i,"DIV",{class:!0});var wo=s(kt);T(rs.$$.fragment,wo),vy=m(wo),$c=a(wo,"P",{});var c1=s($c);ky=n(c1,`RobertaPreLayerNorm Model with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.`),c1.forEach(t),Ty=m(wo),as=a(wo,"P",{});var Tu=s(as);wy=n(Tu,"This model inherits from "),Ul=a(Tu,"A",{href:!0});var p1=s(Ul);$y=n(p1,"PreTrainedModel"),p1.forEach(t),Fy=n(Tu,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Tu.forEach(t),Py=m(wo),ss=a(wo,"P",{});var wu=s(ss);Ny=n(wu,"This model is also a PyTorch "),is=a(wu,"A",{href:!0,rel:!0});var m1=s(is);Ly=n(m1,"torch.nn.Module"),m1.forEach(t),xy=n(wu,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),wu.forEach(t),My=m(wo),Wt=a(wo,"DIV",{class:!0});var ea=s(Wt);T(ls.$$.fragment,ea),Ey=m(ea),Wo=a(ea,"P",{});var bd=s(Wo);Ry=n(bd,"The "),Ql=a(bd,"A",{href:!0});var h1=s(Ql);zy=n(h1,"RobertaPreLayerNormForTokenClassification"),h1.forEach(t),Cy=n(bd," forward method, overrides the "),Fc=a(bd,"CODE",{});var u1=s(Fc);qy=n(u1,"__call__"),u1.forEach(t),jy=n(bd," special method."),bd.forEach(t),Ay=m(ea),T(Kn.$$.fragment,ea),Oy=m(ea),T(Vn.$$.fragment,ea),ea.forEach(t),wo.forEach(t),Ph=m(i),Uo=a(i,"H2",{class:!0});var $u=s(Uo);Jn=a($u,"A",{id:!0,class:!0,href:!0});var f1=s(Jn);Pc=a(f1,"SPAN",{});var g1=s(Pc);T(ds.$$.fragment,g1),g1.forEach(t),f1.forEach(t),Iy=m($u),Nc=a($u,"SPAN",{});var _1=s(Nc);Dy=n(_1,"RobertaPreLayerNormForQuestionAnswering"),_1.forEach(t),$u.forEach(t),Nh=m(i),Tt=a(i,"DIV",{class:!0});var $o=s(Tt);T(cs.$$.fragment,$o),Sy=m($o),Qo=a($o,"P",{});var vd=s(Qo);Wy=n(vd,`RobertaPreLayerNorm Model with a span classification head on top for extractive question-answering tasks like SQuAD
(a linear layers on top of the hidden-states output to compute `),Lc=a(vd,"CODE",{});var y1=s(Lc);Uy=n(y1,"span start logits"),y1.forEach(t),Qy=n(vd," and "),xc=a(vd,"CODE",{});var b1=s(xc);Hy=n(b1,"span end logits"),b1.forEach(t),By=n(vd,")."),vd.forEach(t),Ky=m($o),ps=a($o,"P",{});var Fu=s(ps);Vy=n(Fu,"This model inherits from "),Hl=a(Fu,"A",{href:!0});var v1=s(Hl);Jy=n(v1,"PreTrainedModel"),v1.forEach(t),Gy=n(Fu,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Fu.forEach(t),Xy=m($o),ms=a($o,"P",{});var Pu=s(ms);Yy=n(Pu,"This model is also a PyTorch "),hs=a(Pu,"A",{href:!0,rel:!0});var k1=s(hs);Zy=n(k1,"torch.nn.Module"),k1.forEach(t),eb=n(Pu,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),Pu.forEach(t),tb=m($o),Ut=a($o,"DIV",{class:!0});var ta=s(Ut);T(us.$$.fragment,ta),ob=m(ta),Ho=a(ta,"P",{});var kd=s(Ho);nb=n(kd,"The "),Bl=a(kd,"A",{href:!0});var T1=s(Bl);rb=n(T1,"RobertaPreLayerNormForQuestionAnswering"),T1.forEach(t),ab=n(kd," forward method, overrides the "),Mc=a(kd,"CODE",{});var w1=s(Mc);sb=n(w1,"__call__"),w1.forEach(t),ib=n(kd," special method."),kd.forEach(t),lb=m(ta),T(Gn.$$.fragment,ta),db=m(ta),T(Xn.$$.fragment,ta),ta.forEach(t),$o.forEach(t),Lh=m(i),Bo=a(i,"H2",{class:!0});var Nu=s(Bo);Yn=a(Nu,"A",{id:!0,class:!0,href:!0});var $1=s(Yn);Ec=a($1,"SPAN",{});var F1=s(Ec);T(fs.$$.fragment,F1),F1.forEach(t),$1.forEach(t),cb=m(Nu),Rc=a(Nu,"SPAN",{});var P1=s(Rc);pb=n(P1,"TFRobertaPreLayerNormModel"),P1.forEach(t),Nu.forEach(t),xh=m(i),lt=a(i,"DIV",{class:!0});var eo=s(lt);T(gs.$$.fragment,eo),mb=m(eo),zc=a(eo,"P",{});var N1=s(zc);hb=n(N1,"The bare RoBERTa-PreLayerNorm Model transformer outputting raw hidden-states without any specific head on top."),N1.forEach(t),ub=m(eo),_s=a(eo,"P",{});var Lu=s(_s);fb=n(Lu,"This model inherits from "),Kl=a(Lu,"A",{href:!0});var L1=s(Kl);gb=n(L1,"TFPreTrainedModel"),L1.forEach(t),_b=n(Lu,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Lu.forEach(t),yb=m(eo),ys=a(eo,"P",{});var xu=s(ys);bb=n(xu,"This model is also a "),bs=a(xu,"A",{href:!0,rel:!0});var x1=s(bs);vb=n(x1,"tf.keras.Model"),x1.forEach(t),kb=n(xu,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),xu.forEach(t),Tb=m(eo),T(Zn.$$.fragment,eo),wb=m(eo),Qt=a(eo,"DIV",{class:!0});var oa=s(Qt);T(vs.$$.fragment,oa),$b=m(oa),Ko=a(oa,"P",{});var Td=s(Ko);Fb=n(Td,"The "),Vl=a(Td,"A",{href:!0});var M1=s(Vl);Pb=n(M1,"TFRobertaPreLayerNormModel"),M1.forEach(t),Nb=n(Td," forward method, overrides the "),Cc=a(Td,"CODE",{});var E1=s(Cc);Lb=n(E1,"__call__"),E1.forEach(t),xb=n(Td," special method."),Td.forEach(t),Mb=m(oa),T(er.$$.fragment,oa),Eb=m(oa),T(tr.$$.fragment,oa),oa.forEach(t),eo.forEach(t),Mh=m(i),Vo=a(i,"H2",{class:!0});var Mu=s(Vo);or=a(Mu,"A",{id:!0,class:!0,href:!0});var R1=s(or);qc=a(R1,"SPAN",{});var z1=s(qc);T(ks.$$.fragment,z1),z1.forEach(t),R1.forEach(t),Rb=m(Mu),jc=a(Mu,"SPAN",{});var C1=s(jc);zb=n(C1,"TFRobertaPreLayerNormForCausalLM"),C1.forEach(t),Mu.forEach(t),Eh=m(i),Jo=a(i,"DIV",{class:!0});var Eu=s(Jo);T(Ts.$$.fragment,Eu),Cb=m(Eu),Ht=a(Eu,"DIV",{class:!0});var na=s(Ht);T(ws.$$.fragment,na),qb=m(na),Go=a(na,"P",{});var wd=s(Go);jb=n(wd,"The "),Jl=a(wd,"A",{href:!0});var q1=s(Jl);Ab=n(q1,"TFRobertaPreLayerNormForCausalLM"),q1.forEach(t),Ob=n(wd," forward method, overrides the "),Ac=a(wd,"CODE",{});var j1=s(Ac);Ib=n(j1,"__call__"),j1.forEach(t),Db=n(wd," special method."),wd.forEach(t),Sb=m(na),T(nr.$$.fragment,na),Wb=m(na),T(rr.$$.fragment,na),na.forEach(t),Eu.forEach(t),Rh=m(i),Xo=a(i,"H2",{class:!0});var Ru=s(Xo);ar=a(Ru,"A",{id:!0,class:!0,href:!0});var A1=s(ar);Oc=a(A1,"SPAN",{});var O1=s(Oc);T($s.$$.fragment,O1),O1.forEach(t),A1.forEach(t),Ub=m(Ru),Ic=a(Ru,"SPAN",{});var I1=s(Ic);Qb=n(I1,"TFRobertaPreLayerNormForMaskedLM"),I1.forEach(t),Ru.forEach(t),zh=m(i),dt=a(i,"DIV",{class:!0});var to=s(dt);T(Fs.$$.fragment,to),Hb=m(to),Ps=a(to,"P",{});var zu=s(Ps);Bb=n(zu,"RoBERTa-PreLayerNorm Model with a "),Dc=a(zu,"CODE",{});var D1=s(Dc);Kb=n(D1,"language modeling"),D1.forEach(t),Vb=n(zu," head on top."),zu.forEach(t),Jb=m(to),Ns=a(to,"P",{});var Cu=s(Ns);Gb=n(Cu,"This model inherits from "),Gl=a(Cu,"A",{href:!0});var S1=s(Gl);Xb=n(S1,"TFPreTrainedModel"),S1.forEach(t),Yb=n(Cu,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Cu.forEach(t),Zb=m(to),Ls=a(to,"P",{});var qu=s(Ls);ev=n(qu,"This model is also a "),xs=a(qu,"A",{href:!0,rel:!0});var W1=s(xs);tv=n(W1,"tf.keras.Model"),W1.forEach(t),ov=n(qu,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),qu.forEach(t),nv=m(to),T(sr.$$.fragment,to),rv=m(to),Ft=a(to,"DIV",{class:!0});var Fo=s(Ft);T(Ms.$$.fragment,Fo),av=m(Fo),Yo=a(Fo,"P",{});var $d=s(Yo);sv=n($d,"The "),Xl=a($d,"A",{href:!0});var U1=s(Xl);iv=n(U1,"TFRobertaPreLayerNormForMaskedLM"),U1.forEach(t),lv=n($d," forward method, overrides the "),Sc=a($d,"CODE",{});var Q1=s(Sc);dv=n(Q1,"__call__"),Q1.forEach(t),cv=n($d," special method."),$d.forEach(t),pv=m(Fo),T(ir.$$.fragment,Fo),mv=m(Fo),T(lr.$$.fragment,Fo),hv=m(Fo),T(dr.$$.fragment,Fo),Fo.forEach(t),to.forEach(t),Ch=m(i),Zo=a(i,"H2",{class:!0});var ju=s(Zo);cr=a(ju,"A",{id:!0,class:!0,href:!0});var H1=s(cr);Wc=a(H1,"SPAN",{});var B1=s(Wc);T(Es.$$.fragment,B1),B1.forEach(t),H1.forEach(t),uv=m(ju),Uc=a(ju,"SPAN",{});var K1=s(Uc);fv=n(K1,"TFRobertaPreLayerNormForSequenceClassification"),K1.forEach(t),ju.forEach(t),qh=m(i),ct=a(i,"DIV",{class:!0});var oo=s(ct);T(Rs.$$.fragment,oo),gv=m(oo),Qc=a(oo,"P",{});var V1=s(Qc);_v=n(V1,`RoBERTa-PreLayerNorm Model transformer with a sequence classification/regression head on top (a linear layer on top
of the pooled output) e.g. for GLUE tasks.`),V1.forEach(t),yv=m(oo),zs=a(oo,"P",{});var Au=s(zs);bv=n(Au,"This model inherits from "),Yl=a(Au,"A",{href:!0});var J1=s(Yl);vv=n(J1,"TFPreTrainedModel"),J1.forEach(t),kv=n(Au,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Au.forEach(t),Tv=m(oo),Cs=a(oo,"P",{});var Ou=s(Cs);wv=n(Ou,"This model is also a "),qs=a(Ou,"A",{href:!0,rel:!0});var G1=s(qs);$v=n(G1,"tf.keras.Model"),G1.forEach(t),Fv=n(Ou,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),Ou.forEach(t),Pv=m(oo),T(pr.$$.fragment,oo),Nv=m(oo),Pt=a(oo,"DIV",{class:!0});var Po=s(Pt);T(js.$$.fragment,Po),Lv=m(Po),en=a(Po,"P",{});var Fd=s(en);xv=n(Fd,"The "),Zl=a(Fd,"A",{href:!0});var X1=s(Zl);Mv=n(X1,"TFRobertaPreLayerNormForSequenceClassification"),X1.forEach(t),Ev=n(Fd," forward method, overrides the "),Hc=a(Fd,"CODE",{});var Y1=s(Hc);Rv=n(Y1,"__call__"),Y1.forEach(t),zv=n(Fd," special method."),Fd.forEach(t),Cv=m(Po),T(mr.$$.fragment,Po),qv=m(Po),T(hr.$$.fragment,Po),jv=m(Po),T(ur.$$.fragment,Po),Po.forEach(t),oo.forEach(t),jh=m(i),tn=a(i,"H2",{class:!0});var Iu=s(tn);fr=a(Iu,"A",{id:!0,class:!0,href:!0});var Z1=s(fr);Bc=a(Z1,"SPAN",{});var eF=s(Bc);T(As.$$.fragment,eF),eF.forEach(t),Z1.forEach(t),Av=m(Iu),Kc=a(Iu,"SPAN",{});var tF=s(Kc);Ov=n(tF,"TFRobertaPreLayerNormForMultipleChoice"),tF.forEach(t),Iu.forEach(t),Ah=m(i),pt=a(i,"DIV",{class:!0});var no=s(pt);T(Os.$$.fragment,no),Iv=m(no),Vc=a(no,"P",{});var oF=s(Vc);Dv=n(oF,`RobertaPreLayerNorm Model with a multiple choice classification head on top (a linear layer on top of the pooled
output and a softmax) e.g. for RocStories/SWAG tasks.`),oF.forEach(t),Sv=m(no),Is=a(no,"P",{});var Du=s(Is);Wv=n(Du,"This model inherits from "),ed=a(Du,"A",{href:!0});var nF=s(ed);Uv=n(nF,"TFPreTrainedModel"),nF.forEach(t),Qv=n(Du,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Du.forEach(t),Hv=m(no),Ds=a(no,"P",{});var Su=s(Ds);Bv=n(Su,"This model is also a "),Ss=a(Su,"A",{href:!0,rel:!0});var rF=s(Ss);Kv=n(rF,"tf.keras.Model"),rF.forEach(t),Vv=n(Su,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),Su.forEach(t),Jv=m(no),T(gr.$$.fragment,no),Gv=m(no),Bt=a(no,"DIV",{class:!0});var ra=s(Bt);T(Ws.$$.fragment,ra),Xv=m(ra),on=a(ra,"P",{});var Pd=s(on);Yv=n(Pd,"The "),td=a(Pd,"A",{href:!0});var aF=s(td);Zv=n(aF,"TFRobertaPreLayerNormForMultipleChoice"),aF.forEach(t),ek=n(Pd," forward method, overrides the "),Jc=a(Pd,"CODE",{});var sF=s(Jc);tk=n(sF,"__call__"),sF.forEach(t),ok=n(Pd," special method."),Pd.forEach(t),nk=m(ra),T(_r.$$.fragment,ra),rk=m(ra),T(yr.$$.fragment,ra),ra.forEach(t),no.forEach(t),Oh=m(i),nn=a(i,"H2",{class:!0});var Wu=s(nn);br=a(Wu,"A",{id:!0,class:!0,href:!0});var iF=s(br);Gc=a(iF,"SPAN",{});var lF=s(Gc);T(Us.$$.fragment,lF),lF.forEach(t),iF.forEach(t),ak=m(Wu),Xc=a(Wu,"SPAN",{});var dF=s(Xc);sk=n(dF,"TFRobertaPreLayerNormForTokenClassification"),dF.forEach(t),Wu.forEach(t),Ih=m(i),mt=a(i,"DIV",{class:!0});var ro=s(mt);T(Qs.$$.fragment,ro),ik=m(ro),Yc=a(ro,"P",{});var cF=s(Yc);lk=n(cF,`RoBERTa-PreLayerNorm Model with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.`),cF.forEach(t),dk=m(ro),Hs=a(ro,"P",{});var Uu=s(Hs);ck=n(Uu,"This model inherits from "),od=a(Uu,"A",{href:!0});var pF=s(od);pk=n(pF,"TFPreTrainedModel"),pF.forEach(t),mk=n(Uu,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Uu.forEach(t),hk=m(ro),Bs=a(ro,"P",{});var Qu=s(Bs);uk=n(Qu,"This model is also a "),Ks=a(Qu,"A",{href:!0,rel:!0});var mF=s(Ks);fk=n(mF,"tf.keras.Model"),mF.forEach(t),gk=n(Qu,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),Qu.forEach(t),_k=m(ro),T(vr.$$.fragment,ro),yk=m(ro),Nt=a(ro,"DIV",{class:!0});var No=s(Nt);T(Vs.$$.fragment,No),bk=m(No),rn=a(No,"P",{});var Nd=s(rn);vk=n(Nd,"The "),nd=a(Nd,"A",{href:!0});var hF=s(nd);kk=n(hF,"TFRobertaPreLayerNormForTokenClassification"),hF.forEach(t),Tk=n(Nd," forward method, overrides the "),Zc=a(Nd,"CODE",{});var uF=s(Zc);wk=n(uF,"__call__"),uF.forEach(t),$k=n(Nd," special method."),Nd.forEach(t),Fk=m(No),T(kr.$$.fragment,No),Pk=m(No),T(Tr.$$.fragment,No),Nk=m(No),T(wr.$$.fragment,No),No.forEach(t),ro.forEach(t),Dh=m(i),an=a(i,"H2",{class:!0});var Hu=s(an);$r=a(Hu,"A",{id:!0,class:!0,href:!0});var fF=s($r);ep=a(fF,"SPAN",{});var gF=s(ep);T(Js.$$.fragment,gF),gF.forEach(t),fF.forEach(t),Lk=m(Hu),tp=a(Hu,"SPAN",{});var _F=s(tp);xk=n(_F,"TFRobertaPreLayerNormForQuestionAnswering"),_F.forEach(t),Hu.forEach(t),Sh=m(i),ht=a(i,"DIV",{class:!0});var ao=s(ht);T(Gs.$$.fragment,ao),Mk=m(ao),sn=a(ao,"P",{});var Ld=s(sn);Ek=n(Ld,`RoBERTa-PreLayerNorm Model with a span classification head on top for extractive question-answering tasks like
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Bu.forEach(t),Dk=m(ao),Ys=a(ao,"P",{});var Ku=s(Ys);Sk=n(Ku,"This model is also a "),Zs=a(Ku,"A",{href:!0,rel:!0});var kF=s(Zs);Wk=n(kF,"tf.keras.Model"),kF.forEach(t),Uk=n(Ku,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),Ku.forEach(t),Qk=m(ao),T(Fr.$$.fragment,ao),Hk=m(ao),Lt=a(ao,"DIV",{class:!0});var Lo=s(Lt);T(ei.$$.fragment,Lo),Bk=m(Lo),ln=a(Lo,"P",{});var xd=s(ln);Kk=n(xd,"The "),ad=a(xd,"A",{href:!0});var TF=s(ad);Vk=n(TF,"TFRobertaPreLayerNormForQuestionAnswering"),TF.forEach(t),Jk=n(xd," forward method, overrides the "),rp=a(xd,"CODE",{});var wF=s(rp);Gk=n(wF,"__call__"),wF.forEach(t),Xk=n(xd," special method."),xd.forEach(t),Yk=m(Lo),T(Pr.$$.fragment,Lo),Zk=m(Lo),T(Nr.$$.fragment,Lo),eT=m(Lo),T(Lr.$$.fragment,Lo),Lo.forEach(t),ao.forEach(t),Wh=m(i),dn=a(i,"H2",{class:!0});var Vu=s(dn);xr=a(Vu,"A",{id:!0,class:!0,href:!0});var $F=s(xr);ap=a($F,"SPAN",{});var FF=s(ap);T(ti.$$.fragment,FF),FF.forEach(t),$F.forEach(t),tT=m(Vu),sp=a(Vu,"SPAN",{});var PF=s(sp);oT=n(PF,"FlaxRobertaPreLayerNormModel"),PF.forEach(t),Vu.forEach(t),Uh=m(i),et=a(i,"DIV",{class:!0});var Mt=s(et);T(oi.$$.fragment,Mt),nT=m(Mt),ip=a(Mt,"P",{});var NF=s(ip);rT=n(NF,"The bare RoBERTa-PreLayerNorm Model transformer outputting raw hidden-states without any specific head on top."),NF.forEach(t),aT=m(Mt),ni=a(Mt,"P",{});var Ju=s(ni);sT=n(Ju,"This model inherits from "),sd=a(Ju,"A",{href:!0});var LF=s(sd);iT=n(LF,"FlaxPreTrainedModel"),LF.forEach(t),lT=n(Ju,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`),Ju.forEach(t),dT=m(Mt),ri=a(Mt,"P",{});var Gu=s(ri);cT=n(Gu,"This model is also a Flax Linen "),ai=a(Gu,"A",{href:!0,rel:!0});var xF=s(ai);pT=n(xF,"flax.linen.Module"),xF.forEach(t),mT=n(Gu,`
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.`),Gu.forEach(t),hT=m(Mt),lp=a(Mt,"P",{});var MF=s(lp);uT=n(MF,"Finally, this model supports inherent JAX features such as:"),MF.forEach(t),fT=m(Mt),io=a(Mt,"UL",{});var aa=s(io);dp=a(aa,"LI",{});var EF=s(dp);si=a(EF,"A",{href:!0,rel:!0});var RF=s(si);gT=n(RF,"Just-In-Time (JIT) compilation"),RF.forEach(t),EF.forEach(t),_T=m(aa),cp=a(aa,"LI",{});var zF=s(cp);ii=a(zF,"A",{href:!0,rel:!0});var CF=s(ii);yT=n(CF,"Automatic Differentiation"),CF.forEach(t),zF.forEach(t),bT=m(aa),pp=a(aa,"LI",{});var qF=s(pp);li=a(qF,"A",{href:!0,rel:!0});var jF=s(li);vT=n(jF,"Vectorization"),jF.forEach(t),qF.forEach(t),kT=m(aa),mp=a(aa,"LI",{});var AF=s(mp);di=a(AF,"A",{href:!0,rel:!0});var OF=s(di);TT=n(OF,"Parallelization"),OF.forEach(t),AF.forEach(t),aa.forEach(t),wT=m(Mt),Kt=a(Mt,"DIV",{class:!0});var sa=s(Kt);T(ci.$$.fragment,sa),$T=m(sa),cn=a(sa,"P",{});var Md=s(cn);FT=n(Md,"The "),hp=a(Md,"CODE",{});var IF=s(hp);PT=n(IF,"FlaxRobertaPreLayerNormPreTrainedModel"),IF.forEach(t),NT=n(Md," forward method, overrides the "),up=a(Md,"CODE",{});var DF=s(up);LT=n(DF,"__call__"),DF.forEach(t),xT=n(Md," special method."),Md.forEach(t),MT=m(sa),T(Mr.$$.fragment,sa),ET=m(sa),T(Er.$$.fragment,sa),sa.forEach(t),Mt.forEach(t),Qh=m(i),pn=a(i,"H2",{class:!0});var Xu=s(pn);Rr=a(Xu,"A",{id:!0,class:!0,href:!0});var SF=s(Rr);fp=a(SF,"SPAN",{});var WF=s(fp);T(pi.$$.fragment,WF),WF.forEach(t),SF.forEach(t),RT=m(Xu),gp=a(Xu,"SPAN",{});var UF=s(gp);zT=n(UF,"FlaxRobertaPreLayerNormForCausalLM"),UF.forEach(t),Xu.forEach(t),Hh=m(i),tt=a(i,"DIV",{class:!0});var Et=s(tt);T(mi.$$.fragment,Et),CT=m(Et),_p=a(Et,"P",{});var QF=s(_p);qT=n(QF,`RobertaPreLayerNorm Model with a language modeling head on top (a linear layer on top of the hidden-states output)
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library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`),Yu.forEach(t),DT=m(Et),ui=a(Et,"P",{});var Zu=s(ui);ST=n(Zu,"This model is also a Flax Linen "),fi=a(Zu,"A",{href:!0,rel:!0});var BF=s(fi);WT=n(BF,"flax.linen.Module"),BF.forEach(t),UT=n(Zu,`
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.`),Zu.forEach(t),QT=m(Et),yp=a(Et,"P",{});var KF=s(yp);HT=n(KF,"Finally, this model supports inherent JAX features such as:"),KF.forEach(t),BT=m(Et),lo=a(Et,"UL",{});var ia=s(lo);bp=a(ia,"LI",{});var VF=s(bp);gi=a(VF,"A",{href:!0,rel:!0});var JF=s(gi);KT=n(JF,"Just-In-Time (JIT) compilation"),JF.forEach(t),VF.forEach(t),VT=m(ia),vp=a(ia,"LI",{});var GF=s(vp);_i=a(GF,"A",{href:!0,rel:!0});var XF=s(_i);JT=n(XF,"Automatic Differentiation"),XF.forEach(t),GF.forEach(t),GT=m(ia),kp=a(ia,"LI",{});var YF=s(kp);yi=a(YF,"A",{href:!0,rel:!0});var ZF=s(yi);XT=n(ZF,"Vectorization"),ZF.forEach(t),YF.forEach(t),YT=m(ia),Tp=a(ia,"LI",{});var eP=s(Tp);bi=a(eP,"A",{href:!0,rel:!0});var tP=s(bi);ZT=n(tP,"Parallelization"),tP.forEach(t),eP.forEach(t),ia.forEach(t),ew=m(Et),Vt=a(Et,"DIV",{class:!0});var la=s(Vt);T(vi.$$.fragment,la),tw=m(la),mn=a(la,"P",{});var Ed=s(mn);ow=n(Ed,"The "),wp=a(Ed,"CODE",{});var oP=s(wp);nw=n(oP,"FlaxRobertaPreLayerNormPreTrainedModel"),oP.forEach(t),rw=n(Ed," forward method, overrides the "),$p=a(Ed,"CODE",{});var nP=s($p);aw=n(nP,"__call__"),nP.forEach(t),sw=n(Ed," special method."),Ed.forEach(t),iw=m(la),T(zr.$$.fragment,la),lw=m(la),T(Cr.$$.fragment,la),la.forEach(t),Et.forEach(t),Bh=m(i),hn=a(i,"H2",{class:!0});var ef=s(hn);qr=a(ef,"A",{id:!0,class:!0,href:!0});var rP=s(qr);Fp=a(rP,"SPAN",{});var aP=s(Fp);T(ki.$$.fragment,aP),aP.forEach(t),rP.forEach(t),dw=m(ef),Pp=a(ef,"SPAN",{});var sP=s(Pp);cw=n(sP,"FlaxRobertaPreLayerNormForMaskedLM"),sP.forEach(t),ef.forEach(t),Kh=m(i),ot=a(i,"DIV",{class:!0});var Rt=s(ot);T(Ti.$$.fragment,Rt),pw=m(Rt),wi=a(Rt,"P",{});var tf=s(wi);mw=n(tf,"RoBERTa-PreLayerNorm Model with a "),Np=a(tf,"CODE",{});var iP=s(Np);hw=n(iP,"language modeling"),iP.forEach(t),uw=n(tf," head on top."),tf.forEach(t),fw=m(Rt),$i=a(Rt,"P",{});var of=s($i);gw=n(of,"This model inherits from "),ld=a(of,"A",{href:!0});var lP=s(ld);_w=n(lP,"FlaxPreTrainedModel"),lP.forEach(t),yw=n(of,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`),of.forEach(t),bw=m(Rt),Fi=a(Rt,"P",{});var nf=s(Fi);vw=n(nf,"This model is also a Flax Linen "),Pi=a(nf,"A",{href:!0,rel:!0});var dP=s(Pi);kw=n(dP,"flax.linen.Module"),dP.forEach(t),Tw=n(nf,`
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.`),nf.forEach(t),ww=m(Rt),Lp=a(Rt,"P",{});var cP=s(Lp);$w=n(cP,"Finally, this model supports inherent JAX features such as:"),cP.forEach(t),Fw=m(Rt),co=a(Rt,"UL",{});var da=s(co);xp=a(da,"LI",{});var pP=s(xp);Ni=a(pP,"A",{href:!0,rel:!0});var mP=s(Ni);Pw=n(mP,"Just-In-Time (JIT) compilation"),mP.forEach(t),pP.forEach(t),Nw=m(da),Mp=a(da,"LI",{});var hP=s(Mp);Li=a(hP,"A",{href:!0,rel:!0});var uP=s(Li);Lw=n(uP,"Automatic Differentiation"),uP.forEach(t),hP.forEach(t),xw=m(da),Ep=a(da,"LI",{});var fP=s(Ep);xi=a(fP,"A",{href:!0,rel:!0});var gP=s(xi);Mw=n(gP,"Vectorization"),gP.forEach(t),fP.forEach(t),Ew=m(da),Rp=a(da,"LI",{});var _P=s(Rp);Mi=a(_P,"A",{href:!0,rel:!0});var yP=s(Mi);Rw=n(yP,"Parallelization"),yP.forEach(t),_P.forEach(t),da.forEach(t),zw=m(Rt),Jt=a(Rt,"DIV",{class:!0});var ca=s(Jt);T(Ei.$$.fragment,ca),Cw=m(ca),un=a(ca,"P",{});var Rd=s(un);qw=n(Rd,"The "),zp=a(Rd,"CODE",{});var bP=s(zp);jw=n(bP,"FlaxRobertaPreLayerNormPreTrainedModel"),bP.forEach(t),Aw=n(Rd," forward method, overrides the "),Cp=a(Rd,"CODE",{});var vP=s(Cp);Ow=n(vP,"__call__"),vP.forEach(t),Iw=n(Rd," special method."),Rd.forEach(t),Dw=m(ca),T(jr.$$.fragment,ca),Sw=m(ca),T(Ar.$$.fragment,ca),ca.forEach(t),Rt.forEach(t),Vh=m(i),fn=a(i,"H2",{class:!0});var rf=s(fn);Or=a(rf,"A",{id:!0,class:!0,href:!0});var kP=s(Or);qp=a(kP,"SPAN",{});var TP=s(qp);T(Ri.$$.fragment,TP),TP.forEach(t),kP.forEach(t),Ww=m(rf),jp=a(rf,"SPAN",{});var wP=s(jp);Uw=n(wP,"FlaxRobertaPreLayerNormForSequenceClassification"),wP.forEach(t),rf.forEach(t),Jh=m(i),nt=a(i,"DIV",{class:!0});var zt=s(nt);T(zi.$$.fragment,zt),Qw=m(zt),Ap=a(zt,"P",{});var $P=s(Ap);Hw=n($P,`RobertaPreLayerNorm Model transformer with a sequence classification/regression head on top (a linear layer on top
of the pooled output) e.g. for GLUE tasks.`),$P.forEach(t),Bw=m(zt),Ci=a(zt,"P",{});var af=s(Ci);Kw=n(af,"This model inherits from "),dd=a(af,"A",{href:!0});var FP=s(dd);Vw=n(FP,"FlaxPreTrainedModel"),FP.forEach(t),Jw=n(af,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`),af.forEach(t),Gw=m(zt),qi=a(zt,"P",{});var sf=s(qi);Xw=n(sf,"This model is also a Flax Linen "),ji=a(sf,"A",{href:!0,rel:!0});var PP=s(ji);Yw=n(PP,"flax.linen.Module"),PP.forEach(t),Zw=n(sf,`
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.`),sf.forEach(t),e0=m(zt),Op=a(zt,"P",{});var NP=s(Op);t0=n(NP,"Finally, this model supports inherent JAX features such as:"),NP.forEach(t),o0=m(zt),po=a(zt,"UL",{});var pa=s(po);Ip=a(pa,"LI",{});var LP=s(Ip);Ai=a(LP,"A",{href:!0,rel:!0});var xP=s(Ai);n0=n(xP,"Just-In-Time (JIT) compilation"),xP.forEach(t),LP.forEach(t),r0=m(pa),Dp=a(pa,"LI",{});var MP=s(Dp);Oi=a(MP,"A",{href:!0,rel:!0});var EP=s(Oi);a0=n(EP,"Automatic Differentiation"),EP.forEach(t),MP.forEach(t),s0=m(pa),Sp=a(pa,"LI",{});var RP=s(Sp);Ii=a(RP,"A",{href:!0,rel:!0});var zP=s(Ii);i0=n(zP,"Vectorization"),zP.forEach(t),RP.forEach(t),l0=m(pa),Wp=a(pa,"LI",{});var CP=s(Wp);Di=a(CP,"A",{href:!0,rel:!0});var qP=s(Di);d0=n(qP,"Parallelization"),qP.forEach(t),CP.forEach(t),pa.forEach(t),c0=m(zt),Gt=a(zt,"DIV",{class:!0});var ma=s(Gt);T(Si.$$.fragment,ma),p0=m(ma),gn=a(ma,"P",{});var zd=s(gn);m0=n(zd,"The "),Up=a(zd,"CODE",{});var jP=s(Up);h0=n(jP,"FlaxRobertaPreLayerNormPreTrainedModel"),jP.forEach(t),u0=n(zd," forward method, overrides the "),Qp=a(zd,"CODE",{});var AP=s(Qp);f0=n(AP,"__call__"),AP.forEach(t),g0=n(zd," special method."),zd.forEach(t),_0=m(ma),T(Ir.$$.fragment,ma),y0=m(ma),T(Dr.$$.fragment,ma),ma.forEach(t),zt.forEach(t),Gh=m(i),_n=a(i,"H2",{class:!0});var lf=s(_n);Sr=a(lf,"A",{id:!0,class:!0,href:!0});var OP=s(Sr);Hp=a(OP,"SPAN",{});var IP=s(Hp);T(Wi.$$.fragment,IP),IP.forEach(t),OP.forEach(t),b0=m(lf),Bp=a(lf,"SPAN",{});var DP=s(Bp);v0=n(DP,"FlaxRobertaPreLayerNormForMultipleChoice"),DP.forEach(t),lf.forEach(t),Xh=m(i),rt=a(i,"DIV",{class:!0});var Ct=s(rt);T(Ui.$$.fragment,Ct),k0=m(Ct),Kp=a(Ct,"P",{});var SP=s(Kp);T0=n(SP,`RobertaPreLayerNorm Model with a multiple choice classification head on top (a linear layer on top of the pooled
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library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`),df.forEach(t),N0=m(Ct),Hi=a(Ct,"P",{});var cf=s(Hi);L0=n(cf,"This model is also a Flax Linen "),Bi=a(cf,"A",{href:!0,rel:!0});var UP=s(Bi);x0=n(UP,"flax.linen.Module"),UP.forEach(t),M0=n(cf,`
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.`),cf.forEach(t),E0=m(Ct),Vp=a(Ct,"P",{});var QP=s(Vp);R0=n(QP,"Finally, this model supports inherent JAX features such as:"),QP.forEach(t),z0=m(Ct),mo=a(Ct,"UL",{});var ha=s(mo);Jp=a(ha,"LI",{});var HP=s(Jp);Ki=a(HP,"A",{href:!0,rel:!0});var BP=s(Ki);C0=n(BP,"Just-In-Time (JIT) compilation"),BP.forEach(t),HP.forEach(t),q0=m(ha),Gp=a(ha,"LI",{});var KP=s(Gp);Vi=a(KP,"A",{href:!0,rel:!0});var VP=s(Vi);j0=n(VP,"Automatic Differentiation"),VP.forEach(t),KP.forEach(t),A0=m(ha),Xp=a(ha,"LI",{});var JP=s(Xp);Ji=a(JP,"A",{href:!0,rel:!0});var GP=s(Ji);O0=n(GP,"Vectorization"),GP.forEach(t),JP.forEach(t),I0=m(ha),Yp=a(ha,"LI",{});var XP=s(Yp);Gi=a(XP,"A",{href:!0,rel:!0});var YP=s(Gi);D0=n(YP,"Parallelization"),YP.forEach(t),XP.forEach(t),ha.forEach(t),S0=m(Ct),Xt=a(Ct,"DIV",{class:!0});var ua=s(Xt);T(Xi.$$.fragment,ua),W0=m(ua),yn=a(ua,"P",{});var Cd=s(yn);U0=n(Cd,"The "),Zp=a(Cd,"CODE",{});var ZP=s(Zp);Q0=n(ZP,"FlaxRobertaPreLayerNormPreTrainedModel"),ZP.forEach(t),H0=n(Cd," forward method, overrides the "),em=a(Cd,"CODE",{});var eN=s(em);B0=n(eN,"__call__"),eN.forEach(t),K0=n(Cd," special method."),Cd.forEach(t),V0=m(ua),T(Wr.$$.fragment,ua),J0=m(ua),T(Ur.$$.fragment,ua),ua.forEach(t),Ct.forEach(t),Yh=m(i),bn=a(i,"H2",{class:!0});var pf=s(bn);Qr=a(pf,"A",{id:!0,class:!0,href:!0});var tN=s(Qr);tm=a(tN,"SPAN",{});var oN=s(tm);T(Yi.$$.fragment,oN),oN.forEach(t),tN.forEach(t),G0=m(pf),om=a(pf,"SPAN",{});var nN=s(om);X0=n(nN,"FlaxRobertaPreLayerNormForTokenClassification"),nN.forEach(t),pf.forEach(t),Zh=m(i),at=a(i,"DIV",{class:!0});var qt=s(at);T(Zi.$$.fragment,qt),Y0=m(qt),nm=a(qt,"P",{});var rN=s(nm);Z0=n(rN,`RobertaPreLayerNorm Model with a token classification head on top (a linear layer on top of the hidden-states
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