import{S as DT,i as NT,s as IT,e as s,k as p,w as T,t as o,M as ST,c as r,d as t,m,a,x as w,h as n,b as g,G as e,g as _,y as $,q as F,o as E,B as R,v as WT,L as Xe}from"../../chunks/vendor-hf-doc-builder.js";import{T as Ze}from"../../chunks/Tip-hf-doc-builder.js";import{D as ne}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Ye}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as et}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Ge}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function UT(z){let d,k,h,u,b;return u=new Ye({props:{code:`from transformers import RemBertModel, RemBertConfig

# Initializing a RemBERT rembert style configuration
configuration = RemBertConfig()

# Initializing a model from the rembert style configuration
model = RemBertModel(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> RemBertModel, RemBertConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a RemBERT rembert style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = RemBertConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the rembert style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertModel(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=s("p"),k=o("Example:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function KT(z){let d,k,h,u,b;return u=new Ye({props:{code:`0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |`,highlighted:`0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1 1
| first sequence    | second sequence |`}}),{c(){d=s("p"),k=o("sequence pair mask has the following format:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"sequence pair mask has the following format:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function HT(z){let d,k,h,u,b;return u=new Ye({props:{code:`0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |`,highlighted:`0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1 1
| first sequence    | second sequence |`}}),{c(){d=s("p"),k=o("sequence pair mask has the following format:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"sequence pair mask has the following format:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function QT(z){let d,k,h,u,b;return{c(){d=s("p"),k=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),b=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=r(l,"P",{});var c=a(d);k=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var C=a(h);u=n(C,"Module"),C.forEach(t),b=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){_(l,d,c),e(d,k),e(d,h),e(h,u),e(d,b)},d(l){l&&t(d)}}}function VT(z){let d,k,h,u,b;return u=new Ye({props:{code:`from transformers import AutoTokenizer, RemBertModel
import torch

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
model = RemBertModel.from_pretrained("google/rembert")

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, RemBertModel
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertModel.from_pretrained(<span class="hljs-string">&quot;google/rembert&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=s("p"),k=o("Example:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function JT(z){let d,k,h,u,b;return{c(){d=s("p"),k=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),b=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=r(l,"P",{});var c=a(d);k=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var C=a(h);u=n(C,"Module"),C.forEach(t),b=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){_(l,d,c),e(d,k),e(d,h),e(h,u),e(d,b)},d(l){l&&t(d)}}}function GT(z){let d,k,h,u,b;return u=new Ye({props:{code:`from transformers import AutoTokenizer, RemBertForCausalLM, RemBertConfig
import torch

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
config = RemBertConfig.from_pretrained("google/rembert")
config.is_decoder = True
model = RemBertForCausalLM.from_pretrained("google/rembert", 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, RemBertForCausalLM, RemBertConfig
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config = RemBertConfig.from_pretrained(<span class="hljs-string">&quot;google/rembert&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 = RemBertForCausalLM.from_pretrained(<span class="hljs-string">&quot;google/rembert&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=s("p"),k=o("Example:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function XT(z){let d,k,h,u,b;return{c(){d=s("p"),k=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),b=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=r(l,"P",{});var c=a(d);k=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var C=a(h);u=n(C,"Module"),C.forEach(t),b=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){_(l,d,c),e(d,k),e(d,h),e(h,u),e(d,b)},d(l){l&&t(d)}}}function YT(z){let d,k,h,u,b;return u=new Ye({props:{code:`from transformers import AutoTokenizer, RemBertForMaskedLM
import torch

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
model = RemBertForMaskedLM.from_pretrained("google/rembert")

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)

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)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, RemBertForMaskedLM
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google/rembert&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;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 [MASK]</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>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-[MASK] 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)`}}),{c(){d=s("p"),k=o("Example:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function ZT(z){let d,k,h,u,b;return{c(){d=s("p"),k=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),b=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=r(l,"P",{});var c=a(d);k=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var C=a(h);u=n(C,"Module"),C.forEach(t),b=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){_(l,d,c),e(d,k),e(d,h),e(h,u),e(d,b)},d(l){l&&t(d)}}}function ew(z){let d,k,h,u,b;return u=new Ye({props:{code:`import torch
from transformers import AutoTokenizer, RemBertForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
model = RemBertForSequenceClassification.from_pretrained("google/rembert")

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 = RemBertForSequenceClassification.from_pretrained("google/rembert", 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, RemBertForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google/rembert&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 = RemBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google/rembert&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=s("p"),k=o("Example of single-label classification:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example of single-label classification:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function tw(z){let d,k,h,u,b;return u=new Ye({props:{code:`import torch
from transformers import AutoTokenizer, RemBertForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
model = RemBertForSequenceClassification.from_pretrained("google/rembert", 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 = RemBertForSequenceClassification.from_pretrained(
    "google/rembert", 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, RemBertForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google/rembert&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 = RemBertForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;google/rembert&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=s("p"),k=o("Example of multi-label classification:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example of multi-label classification:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function ow(z){let d,k,h,u,b;return{c(){d=s("p"),k=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),b=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=r(l,"P",{});var c=a(d);k=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var C=a(h);u=n(C,"Module"),C.forEach(t),b=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){_(l,d,c),e(d,k),e(d,h),e(h,u),e(d,b)},d(l){l&&t(d)}}}function nw(z){let d,k,h,u,b;return u=new Ye({props:{code:`from transformers import AutoTokenizer, RemBertForMultipleChoice
import torch

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
model = RemBertForMultipleChoice.from_pretrained("google/rembert")

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, RemBertForMultipleChoice
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google/rembert&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=s("p"),k=o("Example:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function sw(z){let d,k,h,u,b;return{c(){d=s("p"),k=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),b=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=r(l,"P",{});var c=a(d);k=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var C=a(h);u=n(C,"Module"),C.forEach(t),b=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){_(l,d,c),e(d,k),e(d,h),e(h,u),e(d,b)},d(l){l&&t(d)}}}function rw(z){let d,k,h,u,b;return u=new Ye({props:{code:`from transformers import AutoTokenizer, RemBertForTokenClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
model = RemBertForTokenClassification.from_pretrained("google/rembert")

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, RemBertForTokenClassification
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google/rembert&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=s("p"),k=o("Example:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function aw(z){let d,k,h,u,b;return{c(){d=s("p"),k=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),b=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=r(l,"P",{});var c=a(d);k=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var C=a(h);u=n(C,"Module"),C.forEach(t),b=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){_(l,d,c),e(d,k),e(d,h),e(h,u),e(d,b)},d(l){l&&t(d)}}}function iw(z){let d,k,h,u,b;return u=new Ye({props:{code:`from transformers import AutoTokenizer, RemBertForQuestionAnswering
import torch

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
model = RemBertForQuestionAnswering.from_pretrained("google/rembert")

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, RemBertForQuestionAnswering
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/rembert&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=s("p"),k=o("Example:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function lw(z){let d,k,h,u,b,l,c,C,$e,ge,N,ae,te,y,Fe,Q,Ee,_e,S,Re,le,G,Be,ke,U,Me,be,K,me,ze,oe,q,j,ve,H,Ce,ye,I,ie,he,x,qe,V,xe,Te,L,ue,de,D,je,J,ce,se,A,fe,P,Ae,Pe;return{c(){d=s("p"),k=o("TensorFlow models and layers in "),h=s("code"),u=o("transformers"),b=o(" accept two formats as input:"),l=p(),c=s("ul"),C=s("li"),$e=o("having all inputs as keyword arguments (like PyTorch models), or"),ge=p(),N=s("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),y=s("p"),Fe=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=s("code"),Ee=o("model.fit()"),_e=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),S=s("code"),Re=o("model.fit()"),le=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),G=s("code"),Be=o("fit()"),ke=o(" and "),U=s("code"),Me=o("predict()"),be=o(`, such as when creating your own layers or models with
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`),V=s("code"),xe=o("model([input_ids, attention_mask])"),Te=o(" or "),L=s("code"),ue=o("model([input_ids, attention_mask, token_type_ids])"),de=p(),D=s("li"),je=o(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
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`),P=s("a"),Ae=o("subclassing"),Pe=o(` then you don\u2019t need to worry
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pass your inputs and labels in any format that `),S=r(M,"CODE",{});var Ie=a(S);Re=n(Ie,"model.fit()"),Ie.forEach(t),le=n(M,` supports! If, however, you want to use the second
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`),P=r(Z,"A",{href:!0,rel:!0});var Je=a(P);Ae=n(Je,"subclassing"),Je.forEach(t),Pe=n(Z,` 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!`),Z.forEach(t),this.h()},h(){g(P,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),g(P,"rel","nofollow")},m(f,B){_(f,d,B),e(d,k),e(d,h),e(h,u),e(d,b),_(f,l,B),_(f,c,B),e(c,C),e(C,$e),e(c,ge),e(c,N),e(N,ae),_(f,te,B),_(f,y,B),e(y,Fe),e(y,Q),e(Q,Ee),e(y,_e),e(y,S),e(S,Re),e(y,le),e(y,G),e(G,Be),e(y,ke),e(y,U),e(U,Me),e(y,be),e(y,K),e(K,me),e(y,ze),_(f,oe,B),_(f,q,B),e(q,j),e(j,ve),e(j,H),e(H,Ce),e(j,ye),e(j,I),e(I,ie),e(q,he),e(q,x),e(x,qe),e(x,V),e(V,xe),e(x,Te),e(x,L),e(L,ue),e(q,de),e(q,D),e(D,je),e(D,J),e(J,ce),_(f,se,B),_(f,A,B),e(A,fe),e(A,P),e(P,Ae),e(A,Pe)},d(f){f&&t(d),f&&t(l),f&&t(c),f&&t(te),f&&t(y),f&&t(oe),f&&t(q),f&&t(se),f&&t(A)}}}function dw(z){let d,k,h,u,b;return{c(){d=s("p"),k=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),b=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=r(l,"P",{});var c=a(d);k=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var C=a(h);u=n(C,"Module"),C.forEach(t),b=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){_(l,d,c),e(d,k),e(d,h),e(h,u),e(d,b)},d(l){l&&t(d)}}}function cw(z){let d,k,h,u,b;return u=new Ye({props:{code:`from transformers import AutoTokenizer, TFRemBertModel
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
model = TFRemBertModel.from_pretrained("google/rembert")

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, TFRemBertModel
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRemBertModel.from_pretrained(<span class="hljs-string">&quot;google/rembert&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=s("p"),k=o("Example:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function pw(z){let d,k,h,u,b,l,c,C,$e,ge,N,ae,te,y,Fe,Q,Ee,_e,S,Re,le,G,Be,ke,U,Me,be,K,me,ze,oe,q,j,ve,H,Ce,ye,I,ie,he,x,qe,V,xe,Te,L,ue,de,D,je,J,ce,se,A,fe,P,Ae,Pe;return{c(){d=s("p"),k=o("TensorFlow models and layers in "),h=s("code"),u=o("transformers"),b=o(" accept two formats as input:"),l=p(),c=s("ul"),C=s("li"),$e=o("having all inputs as keyword arguments (like PyTorch models), or"),ge=p(),N=s("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),y=s("p"),Fe=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=s("code"),Ee=o("model.fit()"),_e=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),S=s("code"),Re=o("model.fit()"),le=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),G=s("code"),Be=o("fit()"),ke=o(" and "),U=s("code"),Me=o("predict()"),be=o(`, such as when creating your own layers or models with
the Keras `),K=s("code"),me=o("Functional"),ze=o(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),oe=p(),q=s("ul"),j=s("li"),ve=o("a single Tensor with "),H=s("code"),Ce=o("input_ids"),ye=o(" only and nothing else: "),I=s("code"),ie=o("model(input_ids)"),he=p(),x=s("li"),qe=o(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
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`),P=s("a"),Ae=o("subclassing"),Pe=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(f){d=r(f,"P",{});var B=a(d);k=n(B,"TensorFlow models and layers in "),h=r(B,"CODE",{});var Ke=a(h);u=n(Ke,"transformers"),Ke.forEach(t),b=n(B," accept two formats as input:"),B.forEach(t),l=m(f),c=r(f,"UL",{});var re=a(c);C=r(re,"LI",{});var Ne=a(C);$e=n(Ne,"having all inputs as keyword arguments (like PyTorch models), or"),Ne.forEach(t),ge=m(re),N=r(re,"LI",{});var Oe=a(N);ae=n(Oe,"having all inputs as a list, tuple or dict in the first positional argument."),Oe.forEach(t),re.forEach(t),te=m(f),y=r(f,"P",{});var M=a(y);Fe=n(M,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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`),J=r(we,"CODE",{});var pe=a(J);ce=n(pe,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),pe.forEach(t),we.forEach(t),O.forEach(t),se=m(f),A=r(f,"P",{});var Z=a(A);fe=n(Z,`Note that when creating models and layers with
`),P=r(Z,"A",{href:!0,rel:!0});var Je=a(P);Ae=n(Je,"subclassing"),Je.forEach(t),Pe=n(Z,` then you don\u2019t need to worry
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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=r(l,"P",{});var c=a(d);k=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var C=a(h);u=n(C,"Module"),C.forEach(t),b=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){_(l,d,c),e(d,k),e(d,h),e(h,u),e(d,b)},d(l){l&&t(d)}}}function hw(z){let d,k,h,u,b;return u=new Ye({props:{code:`from transformers import AutoTokenizer, TFRemBertForMaskedLM
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
model = TFRemBertForMaskedLM.from_pretrained("google/rembert")

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)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFRemBertForMaskedLM
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRemBertForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google/rembert&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;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 [MASK]</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>)`}}),{c(){d=s("p"),k=o("Example:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function uw(z){let d,k;return d=new Ye({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)`,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-[MASK] 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)`}}),{c(){T(d.$$.fragment)},l(h){w(d.$$.fragment,h)},m(h,u){$(d,h,u),k=!0},p:Xe,i(h){k||(F(d.$$.fragment,h),k=!0)},o(h){E(d.$$.fragment,h),k=!1},d(h){R(d,h)}}}function fw(z){let d,k,h,u,b,l,c,C,$e,ge,N,ae,te,y,Fe,Q,Ee,_e,S,Re,le,G,Be,ke,U,Me,be,K,me,ze,oe,q,j,ve,H,Ce,ye,I,ie,he,x,qe,V,xe,Te,L,ue,de,D,je,J,ce,se,A,fe,P,Ae,Pe;return{c(){d=s("p"),k=o("TensorFlow models and layers in "),h=s("code"),u=o("transformers"),b=o(" accept two formats as input:"),l=p(),c=s("ul"),C=s("li"),$e=o("having all inputs as keyword arguments (like PyTorch models), or"),ge=p(),N=s("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),y=s("p"),Fe=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=s("code"),Ee=o("model.fit()"),_e=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),S=s("code"),Re=o("model.fit()"),le=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),G=s("code"),Be=o("fit()"),ke=o(" and "),U=s("code"),Me=o("predict()"),be=o(`, such as when creating your own layers or models with
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`),P=s("a"),Ae=o("subclassing"),Pe=o(` then you don\u2019t need to worry
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and layers. Because of this support, when using methods like `),Q=r(M,"CODE",{});var He=a(Q);Ee=n(He,"model.fit()"),He.forEach(t),_e=n(M,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),S=r(M,"CODE",{});var Ie=a(S);Re=n(Ie,"model.fit()"),Ie.forEach(t),le=n(M,` supports! If, however, you want to use the second
format outside of Keras methods like `),G=r(M,"CODE",{});var De=a(G);Be=n(De,"fit()"),De.forEach(t),ke=n(M," and "),U=r(M,"CODE",{});var Qe=a(U);Me=n(Qe,"predict()"),Qe.forEach(t),be=n(M,`, such as when creating your own layers or models with
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`),V=r(X,"CODE",{});var We=a(V);xe=n(We,"model([input_ids, attention_mask])"),We.forEach(t),Te=n(X," or "),L=r(X,"CODE",{});var Ve=a(L);ue=n(Ve,"model([input_ids, attention_mask, token_type_ids])"),Ve.forEach(t),X.forEach(t),de=m(O),D=r(O,"LI",{});var we=a(D);je=n(we,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),J=r(we,"CODE",{});var pe=a(J);ce=n(pe,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),pe.forEach(t),we.forEach(t),O.forEach(t),se=m(f),A=r(f,"P",{});var Z=a(A);fe=n(Z,`Note that when creating models and layers with
`),P=r(Z,"A",{href:!0,rel:!0});var Je=a(P);Ae=n(Je,"subclassing"),Je.forEach(t),Pe=n(Z,` 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!`),Z.forEach(t),this.h()},h(){g(P,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),g(P,"rel","nofollow")},m(f,B){_(f,d,B),e(d,k),e(d,h),e(h,u),e(d,b),_(f,l,B),_(f,c,B),e(c,C),e(C,$e),e(c,ge),e(c,N),e(N,ae),_(f,te,B),_(f,y,B),e(y,Fe),e(y,Q),e(Q,Ee),e(y,_e),e(y,S),e(S,Re),e(y,le),e(y,G),e(G,Be),e(y,ke),e(y,U),e(U,Me),e(y,be),e(y,K),e(K,me),e(y,ze),_(f,oe,B),_(f,q,B),e(q,j),e(j,ve),e(j,H),e(H,Ce),e(j,ye),e(j,I),e(I,ie),e(q,he),e(q,x),e(x,qe),e(x,V),e(V,xe),e(x,Te),e(x,L),e(L,ue),e(q,de),e(q,D),e(D,je),e(D,J),e(J,ce),_(f,se,B),_(f,A,B),e(A,fe),e(A,P),e(P,Ae),e(A,Pe)},d(f){f&&t(d),f&&t(l),f&&t(c),f&&t(te),f&&t(y),f&&t(oe),f&&t(q),f&&t(se),f&&t(A)}}}function gw(z){let d,k,h,u,b;return u=new Ye({props:{code:`from transformers import AutoTokenizer, TFRemBertForCausalLM
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
model = TFRemBertForCausalLM.from_pretrained("google/rembert")

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, TFRemBertForCausalLM
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRemBertForCausalLM.from_pretrained(<span class="hljs-string">&quot;google/rembert&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=s("p"),k=o("Example:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function _w(z){let d,k,h,u,b,l,c,C,$e,ge,N,ae,te,y,Fe,Q,Ee,_e,S,Re,le,G,Be,ke,U,Me,be,K,me,ze,oe,q,j,ve,H,Ce,ye,I,ie,he,x,qe,V,xe,Te,L,ue,de,D,je,J,ce,se,A,fe,P,Ae,Pe;return{c(){d=s("p"),k=o("TensorFlow models and layers in "),h=s("code"),u=o("transformers"),b=o(" accept two formats as input:"),l=p(),c=s("ul"),C=s("li"),$e=o("having all inputs as keyword arguments (like PyTorch models), or"),ge=p(),N=s("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),y=s("p"),Fe=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=s("code"),Ee=o("model.fit()"),_e=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),S=s("code"),Re=o("model.fit()"),le=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),G=s("code"),Be=o("fit()"),ke=o(" and "),U=s("code"),Me=o("predict()"),be=o(`, such as when creating your own layers or models with
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`),P=s("a"),Ae=o("subclassing"),Pe=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(f){d=r(f,"P",{});var B=a(d);k=n(B,"TensorFlow models and layers in "),h=r(B,"CODE",{});var Ke=a(h);u=n(Ke,"transformers"),Ke.forEach(t),b=n(B," accept two formats as input:"),B.forEach(t),l=m(f),c=r(f,"UL",{});var re=a(c);C=r(re,"LI",{});var Ne=a(C);$e=n(Ne,"having all inputs as keyword arguments (like PyTorch models), or"),Ne.forEach(t),ge=m(re),N=r(re,"LI",{});var Oe=a(N);ae=n(Oe,"having all inputs as a list, tuple or dict in the first positional argument."),Oe.forEach(t),re.forEach(t),te=m(f),y=r(f,"P",{});var M=a(y);Fe=n(M,`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 `),S=r(M,"CODE",{});var Ie=a(S);Re=n(Ie,"model.fit()"),Ie.forEach(t),le=n(M,` supports! If, however, you want to use the second
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`),V=r(X,"CODE",{});var We=a(V);xe=n(We,"model([input_ids, attention_mask])"),We.forEach(t),Te=n(X," or "),L=r(X,"CODE",{});var Ve=a(L);ue=n(Ve,"model([input_ids, attention_mask, token_type_ids])"),Ve.forEach(t),X.forEach(t),de=m(O),D=r(O,"LI",{});var we=a(D);je=n(we,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
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`),P=r(Z,"A",{href:!0,rel:!0});var Je=a(P);Ae=n(Je,"subclassing"),Je.forEach(t),Pe=n(Z,` then you don\u2019t need to worry
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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=r(l,"P",{});var c=a(d);k=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var C=a(h);u=n(C,"Module"),C.forEach(t),b=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){_(l,d,c),e(d,k),e(d,h),e(h,u),e(d,b)},d(l){l&&t(d)}}}function bw(z){let d,k,h,u,b;return u=new Ye({props:{code:`from transformers import AutoTokenizer, TFRemBertForSequenceClassification
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
model = TFRemBertForSequenceClassification.from_pretrained("google/rembert")

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, TFRemBertForSequenceClassification
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRemBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google/rembert&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=s("p"),k=o("Example:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function vw(z){let d,k;return d=new Ye({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 = TFRemBertForSequenceClassification.from_pretrained("google/rembert", 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 = TFRemBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google/rembert&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(){T(d.$$.fragment)},l(h){w(d.$$.fragment,h)},m(h,u){$(d,h,u),k=!0},p:Xe,i(h){k||(F(d.$$.fragment,h),k=!0)},o(h){E(d.$$.fragment,h),k=!1},d(h){R(d,h)}}}function yw(z){let d,k,h,u,b,l,c,C,$e,ge,N,ae,te,y,Fe,Q,Ee,_e,S,Re,le,G,Be,ke,U,Me,be,K,me,ze,oe,q,j,ve,H,Ce,ye,I,ie,he,x,qe,V,xe,Te,L,ue,de,D,je,J,ce,se,A,fe,P,Ae,Pe;return{c(){d=s("p"),k=o("TensorFlow models and layers in "),h=s("code"),u=o("transformers"),b=o(" accept two formats as input:"),l=p(),c=s("ul"),C=s("li"),$e=o("having all inputs as keyword arguments (like PyTorch models), or"),ge=p(),N=s("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),y=s("p"),Fe=o(`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 `),S=r(M,"CODE",{});var Ie=a(S);Re=n(Ie,"model.fit()"),Ie.forEach(t),le=n(M,` supports! If, however, you want to use the second
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`),V=r(X,"CODE",{});var We=a(V);xe=n(We,"model([input_ids, attention_mask])"),We.forEach(t),Te=n(X," or "),L=r(X,"CODE",{});var Ve=a(L);ue=n(Ve,"model([input_ids, attention_mask, token_type_ids])"),Ve.forEach(t),X.forEach(t),de=m(O),D=r(O,"LI",{});var we=a(D);je=n(we,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),J=r(we,"CODE",{});var pe=a(J);ce=n(pe,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),pe.forEach(t),we.forEach(t),O.forEach(t),se=m(f),A=r(f,"P",{});var Z=a(A);fe=n(Z,`Note that when creating models and layers with
`),P=r(Z,"A",{href:!0,rel:!0});var Je=a(P);Ae=n(Je,"subclassing"),Je.forEach(t),Pe=n(Z,` 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!`),Z.forEach(t),this.h()},h(){g(P,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),g(P,"rel","nofollow")},m(f,B){_(f,d,B),e(d,k),e(d,h),e(h,u),e(d,b),_(f,l,B),_(f,c,B),e(c,C),e(C,$e),e(c,ge),e(c,N),e(N,ae),_(f,te,B),_(f,y,B),e(y,Fe),e(y,Q),e(Q,Ee),e(y,_e),e(y,S),e(S,Re),e(y,le),e(y,G),e(G,Be),e(y,ke),e(y,U),e(U,Me),e(y,be),e(y,K),e(K,me),e(y,ze),_(f,oe,B),_(f,q,B),e(q,j),e(j,ve),e(j,H),e(H,Ce),e(j,ye),e(j,I),e(I,ie),e(q,he),e(q,x),e(x,qe),e(x,V),e(V,xe),e(x,Te),e(x,L),e(L,ue),e(q,de),e(q,D),e(D,je),e(D,J),e(J,ce),_(f,se,B),_(f,A,B),e(A,fe),e(A,P),e(P,Ae),e(A,Pe)},d(f){f&&t(d),f&&t(l),f&&t(c),f&&t(te),f&&t(y),f&&t(oe),f&&t(q),f&&t(se),f&&t(A)}}}function Tw(z){let d,k,h,u,b;return{c(){d=s("p"),k=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),b=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=r(l,"P",{});var c=a(d);k=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var C=a(h);u=n(C,"Module"),C.forEach(t),b=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){_(l,d,c),e(d,k),e(d,h),e(h,u),e(d,b)},d(l){l&&t(d)}}}function ww(z){let d,k,h,u,b;return u=new Ye({props:{code:`from transformers import AutoTokenizer, TFRemBertForMultipleChoice
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
model = TFRemBertForMultipleChoice.from_pretrained("google/rembert")

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, TFRemBertForMultipleChoice
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRemBertForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google/rembert&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=s("p"),k=o("Example:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function $w(z){let d,k,h,u,b,l,c,C,$e,ge,N,ae,te,y,Fe,Q,Ee,_e,S,Re,le,G,Be,ke,U,Me,be,K,me,ze,oe,q,j,ve,H,Ce,ye,I,ie,he,x,qe,V,xe,Te,L,ue,de,D,je,J,ce,se,A,fe,P,Ae,Pe;return{c(){d=s("p"),k=o("TensorFlow models and layers in "),h=s("code"),u=o("transformers"),b=o(" accept two formats as input:"),l=p(),c=s("ul"),C=s("li"),$e=o("having all inputs as keyword arguments (like PyTorch models), or"),ge=p(),N=s("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),y=s("p"),Fe=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=s("code"),Ee=o("model.fit()"),_e=o(` things should \u201Cjust work\u201D for you - just
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`),J=s("code"),ce=o('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),se=p(),A=s("p"),fe=o(`Note that when creating models and layers with
`),P=s("a"),Ae=o("subclassing"),Pe=o(` then you don\u2019t need to worry
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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=r(l,"P",{});var c=a(d);k=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var C=a(h);u=n(C,"Module"),C.forEach(t),b=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){_(l,d,c),e(d,k),e(d,h),e(h,u),e(d,b)},d(l){l&&t(d)}}}function Ew(z){let d,k,h,u,b;return u=new Ye({props:{code:`from transformers import AutoTokenizer, TFRemBertForTokenClassification
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
model = TFRemBertForTokenClassification.from_pretrained("google/rembert")

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, TFRemBertForTokenClassification
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRemBertForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google/rembert&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=s("p"),k=o("Example:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function Rw(z){let d,k;return d=new Ye({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(){T(d.$$.fragment)},l(h){w(d.$$.fragment,h)},m(h,u){$(d,h,u),k=!0},p:Xe,i(h){k||(F(d.$$.fragment,h),k=!0)},o(h){E(d.$$.fragment,h),k=!1},d(h){R(d,h)}}}function Bw(z){let d,k,h,u,b,l,c,C,$e,ge,N,ae,te,y,Fe,Q,Ee,_e,S,Re,le,G,Be,ke,U,Me,be,K,me,ze,oe,q,j,ve,H,Ce,ye,I,ie,he,x,qe,V,xe,Te,L,ue,de,D,je,J,ce,se,A,fe,P,Ae,Pe;return{c(){d=s("p"),k=o("TensorFlow models and layers in "),h=s("code"),u=o("transformers"),b=o(" accept two formats as input:"),l=p(),c=s("ul"),C=s("li"),$e=o("having all inputs as keyword arguments (like PyTorch models), or"),ge=p(),N=s("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),y=s("p"),Fe=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=s("code"),Ee=o("model.fit()"),_e=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),S=s("code"),Re=o("model.fit()"),le=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),G=s("code"),Be=o("fit()"),ke=o(" and "),U=s("code"),Me=o("predict()"),be=o(`, such as when creating your own layers or models with
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`),P=s("a"),Ae=o("subclassing"),Pe=o(` then you don\u2019t need to worry
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`),J=r(we,"CODE",{});var pe=a(J);ce=n(pe,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),pe.forEach(t),we.forEach(t),O.forEach(t),se=m(f),A=r(f,"P",{});var Z=a(A);fe=n(Z,`Note that when creating models and layers with
`),P=r(Z,"A",{href:!0,rel:!0});var Je=a(P);Ae=n(Je,"subclassing"),Je.forEach(t),Pe=n(Z,` 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!`),Z.forEach(t),this.h()},h(){g(P,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),g(P,"rel","nofollow")},m(f,B){_(f,d,B),e(d,k),e(d,h),e(h,u),e(d,b),_(f,l,B),_(f,c,B),e(c,C),e(C,$e),e(c,ge),e(c,N),e(N,ae),_(f,te,B),_(f,y,B),e(y,Fe),e(y,Q),e(Q,Ee),e(y,_e),e(y,S),e(S,Re),e(y,le),e(y,G),e(G,Be),e(y,ke),e(y,U),e(U,Me),e(y,be),e(y,K),e(K,me),e(y,ze),_(f,oe,B),_(f,q,B),e(q,j),e(j,ve),e(j,H),e(H,Ce),e(j,ye),e(j,I),e(I,ie),e(q,he),e(q,x),e(x,qe),e(x,V),e(V,xe),e(x,Te),e(x,L),e(L,ue),e(q,de),e(q,D),e(D,je),e(D,J),e(J,ce),_(f,se,B),_(f,A,B),e(A,fe),e(A,P),e(P,Ae),e(A,Pe)},d(f){f&&t(d),f&&t(l),f&&t(c),f&&t(te),f&&t(y),f&&t(oe),f&&t(q),f&&t(se),f&&t(A)}}}function Mw(z){let d,k,h,u,b;return{c(){d=s("p"),k=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),b=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=r(l,"P",{});var c=a(d);k=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var C=a(h);u=n(C,"Module"),C.forEach(t),b=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){_(l,d,c),e(d,k),e(d,h),e(h,u),e(d,b)},d(l){l&&t(d)}}}function zw(z){let d,k,h,u,b;return u=new Ye({props:{code:`from transformers import AutoTokenizer, TFRemBertForQuestionAnswering
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("google/rembert")
model = TFRemBertForQuestionAnswering.from_pretrained("google/rembert")

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, TFRemBertForQuestionAnswering
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRemBertForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/rembert&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=s("p"),k=o("Example:"),h=p(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);k=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,k),_(l,h,c),$(u,l,c),b=!0},p:Xe,i(l){b||(F(u.$$.fragment,l),b=!0)},o(l){E(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(h),R(u,l)}}}function Cw(z){let d,k;return d=new Ye({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(){T(d.$$.fragment)},l(h){w(d.$$.fragment,h)},m(h,u){$(d,h,u),k=!0},p:Xe,i(h){k||(F(d.$$.fragment,h),k=!0)},o(h){E(d.$$.fragment,h),k=!1},d(h){R(d,h)}}}function qw(z){let d,k,h,u,b,l,c,C,$e,ge,N,ae,te,y,Fe,Q,Ee,_e,S,Re,le,G,Be,ke,U,Me,be,K,me,ze,oe,q,j,ve,H,Ce,ye,I,ie,he,x,qe,V,xe,Te,L,ue,de,D,je,J,ce,se,A,fe,P,Ae,Pe,f,B,Ke,re,Ne,Oe,M,He,Ie,De,Qe,Ue,O,W,Se,Le,X,We,Ve,we,pe,Z,Je,so,kp,pa,bp,vp,ps,yp,Tp,wp,ro,$p,ma,Fp,Ep,ha,Rp,Bp,Mp,Wo,Ud,ao,Uo,ci,ms,zp,pi,Cp,Kd,tt,hs,qp,us,xp,fs,jp,Ap,Pp,gs,Lp,ua,Op,Dp,Np,Jt,_s,Ip,mi,Sp,Wp,ks,fa,Up,hi,Kp,Hp,ga,Qp,ui,Vp,Jp,Ko,bs,Gp,vs,Xp,fi,Yp,Zp,em,Rt,ys,tm,gi,om,nm,Ho,sm,io,rm,_i,am,im,ki,lm,dm,cm,_a,Ts,Hd,lo,Qo,bi,ws,pm,vi,mm,Qd,nt,$s,hm,Kt,um,yi,fm,gm,Fs,_m,km,ka,bm,vm,ym,Gt,Es,Tm,Ti,wm,$m,Rs,ba,Fm,wi,Em,Rm,va,Bm,$i,Mm,zm,Vo,Bs,Cm,Ms,qm,Fi,xm,jm,Am,Bt,zs,Pm,Ei,Lm,Om,Jo,Dm,Ri,Nm,Im,ya,Cs,Vd,co,Go,Bi,qs,Sm,Mi,Wm,Jd,ht,xs,Um,js,Km,As,Hm,Qm,Vm,Ps,Jm,Ls,Gm,Xm,Ym,st,Zm,zi,eh,th,Ci,oh,nh,qi,sh,rh,xi,ah,ih,ji,lh,dh,Ai,ch,ph,mh,Mt,Os,hh,po,uh,Ta,fh,gh,Pi,_h,kh,bh,Xo,vh,Yo,Gd,mo,Zo,Li,Ds,yh,Oi,Th,Xd,Ht,Ns,wh,ho,$h,Di,Fh,Eh,Is,Rh,Bh,Mh,zt,Ss,zh,uo,Ch,wa,qh,xh,Ni,jh,Ah,Ph,en,Lh,tn,Yd,fo,on,Ii,Ws,Oh,Si,Dh,Zd,Qt,Us,Nh,go,Ih,Wi,Sh,Wh,Ks,Uh,Kh,Hh,Ct,Hs,Qh,_o,Vh,$a,Jh,Gh,Ui,Xh,Yh,Zh,nn,eu,sn,ec,ko,rn,Ki,Qs,tu,Hi,ou,tc,wt,Vs,nu,Qi,su,ru,Js,au,Gs,iu,lu,du,ft,Xs,cu,bo,pu,Fa,mu,hu,Vi,uu,fu,gu,an,_u,ln,ku,dn,oc,vo,cn,Ji,Ys,bu,Gi,vu,nc,$t,Zs,yu,Xi,Tu,wu,er,$u,tr,Fu,Eu,Ru,qt,or,Bu,yo,Mu,Ea,zu,Cu,Yi,qu,xu,ju,pn,Au,mn,sc,To,hn,Zi,nr,Pu,el,Lu,rc,Ft,sr,Ou,tl,Du,Nu,rr,Iu,ar,Su,Wu,Uu,xt,ir,Ku,wo,Hu,Ra,Qu,Vu,ol,Ju,Gu,Xu,un,Yu,fn,ac,$o,gn,nl,lr,Zu,sl,ef,ic,Et,dr,tf,Fo,of,rl,nf,sf,al,rf,af,lf,cr,df,pr,cf,pf,mf,jt,mr,hf,Eo,uf,Ba,ff,gf,il,_f,kf,bf,_n,vf,kn,lc,Ro,bn,ll,hr,yf,dl,Tf,dc,rt,ur,wf,cl,$f,Ff,fr,Ef,Ma,Rf,Bf,Mf,gr,zf,_r,Cf,qf,xf,vn,jf,At,kr,Af,Bo,Pf,za,Lf,Of,pl,Df,Nf,If,yn,Sf,Tn,cc,Mo,wn,ml,br,Wf,hl,Uf,pc,at,vr,Kf,yr,Hf,ul,Qf,Vf,Jf,Tr,Gf,Ca,Xf,Yf,Zf,wr,eg,$r,tg,og,ng,$n,sg,gt,Fr,rg,zo,ag,qa,ig,lg,fl,dg,cg,pg,Fn,mg,En,hg,Rn,mc,Co,Bn,gl,Er,ug,_l,fg,hc,it,Rr,gg,Br,_g,kl,kg,bg,vg,Mr,yg,xa,Tg,wg,$g,zr,Fg,Cr,Eg,Rg,Bg,Mn,Mg,_t,qr,zg,ot,Cg,bl,qg,xg,vl,jg,Ag,yl,Pg,Lg,Tl,Og,Dg,wl,Ng,Ig,$l,Sg,Wg,Fl,Ug,Kg,Hg,xr,jr,Qg,El,Vg,Jg,Gg,Ar,Xg,Rl,Yg,Zg,e_,Y,t_,Bl,o_,n_,Ml,s_,r_,zl,a_,i_,Cl,l_,d_,ql,c_,p_,xl,m_,h_,jl,u_,f_,Al,g_,__,Pl,k_,b_,Ll,v_,y_,Ol,T_,w_,Dl,$_,F_,Nl,E_,R_,Il,B_,M_,Sl,z_,C_,Wl,q_,x_,Ul,j_,A_,Kl,P_,L_,Hl,O_,D_,Ql,N_,I_,S_,zn,uc,qo,Cn,Vl,Pr,W_,Jl,U_,fc,lt,Lr,K_,Gl,H_,Q_,Or,V_,ja,J_,G_,X_,Dr,Y_,Nr,Z_,ek,tk,qn,ok,kt,Ir,nk,xo,sk,Aa,rk,ak,Xl,ik,lk,dk,xn,ck,jn,pk,An,gc,jo,Pn,Yl,Sr,mk,Zl,hk,_c,dt,Wr,uk,ed,fk,gk,Ur,_k,Pa,kk,bk,vk,Kr,yk,Hr,Tk,wk,$k,Ln,Fk,Pt,Qr,Ek,Ao,Rk,La,Bk,Mk,td,zk,Ck,qk,On,xk,Dn,kc,Po,Nn,od,Vr,jk,nd,Ak,bc,ct,Jr,Pk,sd,Lk,Ok,Gr,Dk,Oa,Nk,Ik,Sk,Xr,Wk,Yr,Uk,Kk,Hk,In,Qk,bt,Zr,Vk,Lo,Jk,Da,Gk,Xk,rd,Yk,Zk,eb,Sn,tb,Wn,ob,Un,vc,Oo,Kn,ad,ea,nb,id,sb,yc,pt,ta,rb,Do,ab,ld,ib,lb,dd,db,cb,pb,oa,mb,Na,hb,ub,fb,na,gb,sa,_b,kb,bb,Hn,vb,vt,ra,yb,No,Tb,Ia,wb,$b,cd,Fb,Eb,Rb,Qn,Bb,Vn,Mb,Jn,Tc;return l=new et({}),y=new et({}),x=new et({}),Le=new et({}),Z=new ne({props:{name:"class transformers.RemBertConfig",anchor:"transformers.RemBertConfig",parameters:[{name:"vocab_size",val:" = 250300"},{name:"hidden_size",val:" = 1152"},{name:"num_hidden_layers",val:" = 32"},{name:"num_attention_heads",val:" = 18"},{name:"input_embedding_size",val:" = 256"},{name:"output_embedding_size",val:" = 1664"},{name:"intermediate_size",val:" = 4608"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.0"},{name:"attention_probs_dropout_prob",val:" = 0.0"},{name:"classifier_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:"use_cache",val:" = True"},{name:"pad_token_id",val:" = 0"},{name:"bos_token_id",val:" = 312"},{name:"eos_token_id",val:" = 313"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RemBertConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 250300) &#x2014;
Vocabulary size of the RemBERT 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/rembert#transformers.RemBertModel">RemBertModel</a> or <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a>. Vocabulary size of the model.
Defines the different tokens that can be represented by the <em>inputs_ids</em> passed to the forward method of
<a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a>.`,name:"vocab_size"},{anchor:"transformers.RemBertConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1152) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.RemBertConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.RemBertConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 18) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.RemBertConfig.input_embedding_size",description:`<strong>input_embedding_size</strong> (<code>int</code>, <em>optional</em>, defaults to 256) &#x2014;
Dimensionality of the input embeddings.`,name:"input_embedding_size"},{anchor:"transformers.RemBertConfig.output_embedding_size",description:`<strong>output_embedding_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1664) &#x2014;
Dimensionality of the output embeddings.`,name:"output_embedding_size"},{anchor:"transformers.RemBertConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 4608) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.RemBertConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</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;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.RemBertConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0) &#x2014;
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.RemBertConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.RemBertConfig.classifier_dropout_prob",description:`<strong>classifier_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the classifier layer when fine-tuning.`,name:"classifier_dropout_prob"},{anchor:"transformers.RemBertConfig.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.RemBertConfig.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/rembert#transformers.RemBertModel">RemBertModel</a> or <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a>.`,name:"type_vocab_size"},{anchor:"transformers.RemBertConfig.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.RemBertConfig.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.RemBertConfig.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.RemBertConfig.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"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/rembert/configuration_rembert.py#L32"}}),Wo=new Ge({props:{anchor:"transformers.RemBertConfig.example",$$slots:{default:[UT]},$$scope:{ctx:z}}}),ms=new et({}),hs=new ne({props:{name:"class transformers.RemBertTokenizer",anchor:"transformers.RemBertTokenizer",parameters:[{name:"vocab_file",val:""},{name:"do_lower_case",val:" = False"},{name:"remove_space",val:" = True"},{name:"keep_accents",val:" = True"},{name:"bos_token",val:" = '[CLS]'"},{name:"eos_token",val:" = '[SEP]'"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RemBertTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.RemBertTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the <code>cls_token</code>.</p>

					</div>`,name:"bos_token"},{anchor:"transformers.RemBertTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The end of sequence token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the <code>sep_token</code>.</p>

					</div>`,name:"eos_token"},{anchor:"transformers.RemBertTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.RemBertTokenizer.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.`,name:"sep_token"},{anchor:"transformers.RemBertTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.RemBertTokenizer.cls_token",description:`<strong>cls_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.`,name:"cls_token"},{anchor:"transformers.RemBertTokenizer.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[MASK]&quot;</code>) &#x2014;
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.`,name:"mask_token"},{anchor:"transformers.RemBertTokenizer.sp_model",description:`<strong>sp_model</strong> (<code>SentencePieceProcessor</code>) &#x2014;
The <em>SentencePiece</em> processor that is used for every conversion (string, tokens and IDs).`,name:"sp_model"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/rembert/tokenization_rembert.py#L43"}}),_s=new ne({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.RemBertTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.RemBertTokenizer.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.RemBertTokenizer.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/rembert/tokenization_rembert.py#L172",returnDescription:`
<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),bs=new ne({props:{name:"get_special_tokens_mask",anchor:"transformers.RemBertTokenizer.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.RemBertTokenizer.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.RemBertTokenizer.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"},{anchor:"transformers.RemBertTokenizer.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/rembert/tokenization_rembert.py#L197",returnDescription:`
<p>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),ys=new ne({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.RemBertTokenizer.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.RemBertTokenizer.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.RemBertTokenizer.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/rembert/tokenization_rembert.py#L228",returnDescription:`
<p>List of <a href="../glossary#token-type-ids">token type IDs</a> according to the given sequence(s).</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),Ho=new Ge({props:{anchor:"transformers.RemBertTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[KT]},$$scope:{ctx:z}}}),Ts=new ne({props:{name:"save_vocabulary",anchor:"transformers.RemBertTokenizer.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/rembert/tokenization_rembert.py#L258"}}),ws=new et({}),$s=new ne({props:{name:"class transformers.RemBertTokenizerFast",anchor:"transformers.RemBertTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"do_lower_case",val:" = True"},{name:"remove_space",val:" = True"},{name:"keep_accents",val:" = False"},{name:"bos_token",val:" = '[CLS]'"},{name:"eos_token",val:" = '[SEP]'"},{name:"unk_token",val:" = '<unk>'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '<pad>'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RemBertTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.RemBertTokenizerFast.do_lower_case",description:`<strong>do_lower_case</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to lowercase the input when tokenizing.`,name:"do_lower_case"},{anchor:"transformers.RemBertTokenizerFast.remove_space",description:`<strong>remove_space</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).`,name:"remove_space"},{anchor:"transformers.RemBertTokenizerFast.keep_accents",description:`<strong>keep_accents</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to keep accents when tokenizing.`,name:"keep_accents"},{anchor:"transformers.RemBertTokenizerFast.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the <code>cls_token</code>.</p>

					</div>`,name:"bos_token"},{anchor:"transformers.RemBertTokenizerFast.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The end of sequence token. .. note:: When building a sequence using special tokens, this is not the token
that is used for the end of sequence. The token used is the <code>sep_token</code>.`,name:"eos_token"},{anchor:"transformers.RemBertTokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.RemBertTokenizerFast.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.`,name:"sep_token"},{anchor:"transformers.RemBertTokenizerFast.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.RemBertTokenizerFast.cls_token",description:`<strong>cls_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.`,name:"cls_token"},{anchor:"transformers.RemBertTokenizerFast.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[MASK]&quot;</code>) &#x2014;
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.`,name:"mask_token"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L51"}}),Es=new ne({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.RemBertTokenizerFast.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.RemBertTokenizerFast.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added`,name:"token_ids_0"},{anchor:"transformers.RemBertTokenizerFast.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L144",returnDescription:`
<p>list of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),Bs=new ne({props:{name:"get_special_tokens_mask",anchor:"transformers.RemBertTokenizerFast.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.RemBertTokenizerFast.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of ids.`,name:"token_ids_0"},{anchor:"transformers.RemBertTokenizerFast.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"},{anchor:"transformers.RemBertTokenizerFast.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Set to True if the token list is already formatted with special tokens for the model`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L169",returnDescription:`
<p>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),zs=new ne({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.RemBertTokenizerFast.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.RemBertTokenizerFast.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of ids.`,name:"token_ids_0"},{anchor:"transformers.RemBertTokenizerFast.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L200",returnDescription:`
<p>List of <a href="../glossary#token-type-ids">token type IDs</a> according to the given sequence(s).</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),Jo=new Ge({props:{anchor:"transformers.RemBertTokenizerFast.create_token_type_ids_from_sequences.example",$$slots:{default:[HT]},$$scope:{ctx:z}}}),Cs=new ne({props:{name:"save_vocabulary",anchor:"transformers.RemBertTokenizerFast.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L230"}}),qs=new et({}),xs=new ne({props:{name:"class transformers.RemBertModel",anchor:"transformers.RemBertModel",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = True"}],parametersDescription:[{anchor:"transformers.RemBertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/rembert/modeling_rembert.py#L745"}}),Os=new ne({props:{name:"forward",anchor:"transformers.RemBertModel.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = 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:"past_key_values",val:": typing.Optional[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.RemBertModel.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.RemBertModel.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.RemBertModel.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.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RemBertModel.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.RemBertModel.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.RemBertModel.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RemBertModel.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.RemBertModel.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.RemBertModel.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.RemBertModel.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.RemBertModel.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.RemBertModel.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.
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.RemBertModel.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/rembert/modeling_rembert.py#L784",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions</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/rembert#transformers.RemBertConfig"
>RemBertConfig</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>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</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>
<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>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Xo=new Ze({props:{$$slots:{default:[QT]},$$scope:{ctx:z}}}),Yo=new Ge({props:{anchor:"transformers.RemBertModel.forward.example",$$slots:{default:[VT]},$$scope:{ctx:z}}}),Ds=new et({}),Ns=new ne({props:{name:"class transformers.RemBertForCausalLM",anchor:"transformers.RemBertForCausalLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RemBertForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/rembert/modeling_rembert.py#L1016"}}),Ss=new ne({props:{name:"forward",anchor:"transformers.RemBertForCausalLM.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = 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:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = 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.RemBertForCausalLM.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.RemBertForCausalLM.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.RemBertForCausalLM.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.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RemBertForCausalLM.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.RemBertForCausalLM.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.RemBertForCausalLM.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RemBertForCausalLM.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.RemBertForCausalLM.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.RemBertForCausalLM.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.RemBertForCausalLM.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.RemBertForCausalLM.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.RemBertForCausalLM.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.
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.RemBertForCausalLM.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 n <code>[0, ..., config.vocab_size]</code>.`,name:"labels"},{anchor:"transformers.RemBertForCausalLM.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/rembert/modeling_rembert.py#L1037",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/rembert#transformers.RemBertConfig"
>RemBertConfig</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>
`}}),en=new Ze({props:{$$slots:{default:[JT]},$$scope:{ctx:z}}}),tn=new Ge({props:{anchor:"transformers.RemBertForCausalLM.forward.example",$$slots:{default:[GT]},$$scope:{ctx:z}}}),Ws=new et({}),Us=new ne({props:{name:"class transformers.RemBertForMaskedLM",anchor:"transformers.RemBertForMaskedLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RemBertForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/rembert/modeling_rembert.py#L914"}}),Hs=new ne({props:{name:"forward",anchor:"transformers.RemBertForMaskedLM.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = 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.RemBertForMaskedLM.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.RemBertForMaskedLM.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.RemBertForMaskedLM.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.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RemBertForMaskedLM.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.RemBertForMaskedLM.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.RemBertForMaskedLM.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RemBertForMaskedLM.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.RemBertForMaskedLM.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.RemBertForMaskedLM.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.RemBertForMaskedLM.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"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/rembert/modeling_rembert.py#L936",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/rembert#transformers.RemBertConfig"
>RemBertConfig</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>
`}}),nn=new Ze({props:{$$slots:{default:[XT]},$$scope:{ctx:z}}}),sn=new Ge({props:{anchor:"transformers.RemBertForMaskedLM.forward.example",$$slots:{default:[YT]},$$scope:{ctx:z}}}),Qs=new et({}),Vs=new ne({props:{name:"class transformers.RemBertForSequenceClassification",anchor:"transformers.RemBertForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RemBertForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/rembert/modeling_rembert.py#L1168"}}),Xs=new ne({props:{name:"forward",anchor:"transformers.RemBertForSequenceClassification.forward",parameters:[{name:"input_ids",val:": FloatTensor = 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.FloatTensor] = 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.RemBertForSequenceClassification.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.RemBertForSequenceClassification.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.RemBertForSequenceClassification.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.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RemBertForSequenceClassification.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.RemBertForSequenceClassification.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.RemBertForSequenceClassification.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RemBertForSequenceClassification.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.RemBertForSequenceClassification.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.RemBertForSequenceClassification.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.RemBertForSequenceClassification.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/rembert/modeling_rembert.py#L1179",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/rembert#transformers.RemBertConfig"
>RemBertConfig</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>
`}}),an=new Ze({props:{$$slots:{default:[ZT]},$$scope:{ctx:z}}}),ln=new Ge({props:{anchor:"transformers.RemBertForSequenceClassification.forward.example",$$slots:{default:[ew]},$$scope:{ctx:z}}}),dn=new Ge({props:{anchor:"transformers.RemBertForSequenceClassification.forward.example-2",$$slots:{default:[tw]},$$scope:{ctx:z}}}),Ys=new et({}),Zs=new ne({props:{name:"class transformers.RemBertForMultipleChoice",anchor:"transformers.RemBertForMultipleChoice",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RemBertForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/rembert/modeling_rembert.py#L1264"}}),or=new ne({props:{name:"forward",anchor:"transformers.RemBertForMultipleChoice.forward",parameters:[{name:"input_ids",val:": FloatTensor = 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.FloatTensor] = 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.RemBertForMultipleChoice.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.RemBertForMultipleChoice.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.RemBertForMultipleChoice.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.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RemBertForMultipleChoice.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.RemBertForMultipleChoice.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.RemBertForMultipleChoice.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RemBertForMultipleChoice.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.RemBertForMultipleChoice.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.RemBertForMultipleChoice.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.RemBertForMultipleChoice.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/rembert/modeling_rembert.py#L1275",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/rembert#transformers.RemBertConfig"
>RemBertConfig</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>
`}}),pn=new Ze({props:{$$slots:{default:[ow]},$$scope:{ctx:z}}}),mn=new Ge({props:{anchor:"transformers.RemBertForMultipleChoice.forward.example",$$slots:{default:[nw]},$$scope:{ctx:z}}}),nr=new et({}),sr=new ne({props:{name:"class transformers.RemBertForTokenClassification",anchor:"transformers.RemBertForTokenClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RemBertForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/rembert/modeling_rembert.py#L1355"}}),ir=new ne({props:{name:"forward",anchor:"transformers.RemBertForTokenClassification.forward",parameters:[{name:"input_ids",val:": FloatTensor = 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.FloatTensor] = 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.RemBertForTokenClassification.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.RemBertForTokenClassification.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.RemBertForTokenClassification.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.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RemBertForTokenClassification.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.RemBertForTokenClassification.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.RemBertForTokenClassification.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RemBertForTokenClassification.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
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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.RemBertForTokenClassification.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.RemBertForTokenClassification.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/rembert/modeling_rembert.py#L1367",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/rembert#transformers.RemBertConfig"
>RemBertConfig</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>
`}}),un=new Ze({props:{$$slots:{default:[sw]},$$scope:{ctx:z}}}),fn=new Ge({props:{anchor:"transformers.RemBertForTokenClassification.forward.example",$$slots:{default:[rw]},$$scope:{ctx:z}}}),lr=new et({}),dr=new ne({props:{name:"class transformers.RemBertForQuestionAnswering",anchor:"transformers.RemBertForQuestionAnswering",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RemBertForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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
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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.RemBertForQuestionAnswering.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.RemBertForQuestionAnswering.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.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RemBertForQuestionAnswering.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.RemBertForQuestionAnswering.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.RemBertForQuestionAnswering.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RemBertForQuestionAnswering.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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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.RemBertForQuestionAnswering.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.RemBertForQuestionAnswering.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.RemBertForQuestionAnswering.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/rembert/modeling_rembert.py#L1445",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/rembert#transformers.RemBertConfig"
>RemBertConfig</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>
`}}),_n=new Ze({props:{$$slots:{default:[aw]},$$scope:{ctx:z}}}),kn=new Ge({props:{anchor:"transformers.RemBertForQuestionAnswering.forward.example",$$slots:{default:[iw]},$$scope:{ctx:z}}}),hr=new et({}),ur=new ne({props:{name:"class transformers.TFRemBertModel",anchor:"transformers.TFRemBertModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRemBertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/rembert/modeling_tf_rembert.py#L943"}}),vn=new Ze({props:{$$slots:{default:[lw]},$$scope:{ctx:z}}}),kr=new ne({props:{name:"call",anchor:"transformers.TFRemBertModel.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.TFRemBertModel.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the 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.TFRemBertModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</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.TFRemBertModel.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</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.TFRemBertModel.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</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.TFRemBertModel.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</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.TFRemBertModel.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <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.TFRemBertModel.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.TFRemBertModel.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.TFRemBertModel.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.TFRemBertModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#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.TFRemBertModel.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.TFRemBertModel.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.TFRemBertModel.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.TFRemBertModel.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/rembert/modeling_tf_rembert.py#L949",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/rembert#transformers.RemBertConfig"
>RemBertConfig</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>
`}}),yn=new Ze({props:{$$slots:{default:[dw]},$$scope:{ctx:z}}}),Tn=new Ge({props:{anchor:"transformers.TFRemBertModel.call.example",$$slots:{default:[cw]},$$scope:{ctx:z}}}),br=new et({}),vr=new ne({props:{name:"class transformers.TFRemBertForMaskedLM",anchor:"transformers.TFRemBertForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRemBertForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/rembert/modeling_tf_rembert.py#L1035"}}),$n=new Ze({props:{$$slots:{default:[pw]},$$scope:{ctx:z}}}),Fr=new ne({props:{name:"call",anchor:"transformers.TFRemBertForMaskedLM.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.TFRemBertForMaskedLM.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the 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.TFRemBertForMaskedLM.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</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.TFRemBertForMaskedLM.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</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.TFRemBertForMaskedLM.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</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.TFRemBertForMaskedLM.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</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.TFRemBertForMaskedLM.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <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.TFRemBertForMaskedLM.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.TFRemBertForMaskedLM.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.TFRemBertForMaskedLM.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.TFRemBertForMaskedLM.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#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.TFRemBertForMaskedLM.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 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/rembert/modeling_tf_rembert.py#L1051",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/rembert#transformers.RemBertConfig"
>RemBertConfig</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>
`}}),Fn=new Ze({props:{$$slots:{default:[mw]},$$scope:{ctx:z}}}),En=new Ge({props:{anchor:"transformers.TFRemBertForMaskedLM.call.example",$$slots:{default:[hw]},$$scope:{ctx:z}}}),Rn=new Ge({props:{anchor:"transformers.TFRemBertForMaskedLM.call.example-2",$$slots:{default:[uw]},$$scope:{ctx:z}}}),Er=new et({}),Rr=new ne({props:{name:"class transformers.TFRemBertForCausalLM",anchor:"transformers.TFRemBertForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRemBertForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/rembert/modeling_tf_rembert.py#L1115"}}),Mn=new Ze({props:{$$slots:{default:[fw]},$$scope:{ctx:z}}}),qr=new ne({props:{name:"call",anchor:"transformers.TFRemBertForCausalLM.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"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1141",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/rembert#transformers.RemBertConfig"
>RemBertConfig</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>
`}}),zn=new Ge({props:{anchor:"transformers.TFRemBertForCausalLM.call.example",$$slots:{default:[gw]},$$scope:{ctx:z}}}),Pr=new et({}),Lr=new ne({props:{name:"class transformers.TFRemBertForSequenceClassification",anchor:"transformers.TFRemBertForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRemBertForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/rembert/modeling_tf_rembert.py#L1248"}}),qn=new Ze({props:{$$slots:{default:[_w]},$$scope:{ctx:z}}}),Ir=new ne({props:{name:"call",anchor:"transformers.TFRemBertForSequenceClassification.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.TFRemBertForSequenceClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the 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.TFRemBertForSequenceClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</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.TFRemBertForSequenceClassification.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</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.TFRemBertForSequenceClassification.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</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.TFRemBertForSequenceClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</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.TFRemBertForSequenceClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <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.TFRemBertForSequenceClassification.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.TFRemBertForSequenceClassification.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.TFRemBertForSequenceClassification.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.TFRemBertForSequenceClassification.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#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.TFRemBertForSequenceClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> or <code>np.ndarray</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/rembert/modeling_tf_rembert.py#L1262",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/rembert#transformers.RemBertConfig"
>RemBertConfig</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>
`}}),xn=new Ze({props:{$$slots:{default:[kw]},$$scope:{ctx:z}}}),jn=new Ge({props:{anchor:"transformers.TFRemBertForSequenceClassification.call.example",$$slots:{default:[bw]},$$scope:{ctx:z}}}),An=new Ge({props:{anchor:"transformers.TFRemBertForSequenceClassification.call.example-2",$$slots:{default:[vw]},$$scope:{ctx:z}}}),Sr=new et({}),Wr=new ne({props:{name:"class transformers.TFRemBertForMultipleChoice",anchor:"transformers.TFRemBertForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRemBertForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/rembert/modeling_tf_rembert.py#L1331"}}),Ln=new Ze({props:{$$slots:{default:[yw]},$$scope:{ctx:z}}}),Qr=new ne({props:{name:"call",anchor:"transformers.TFRemBertForMultipleChoice.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.TFRemBertForMultipleChoice.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the 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.TFRemBertForMultipleChoice.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</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.TFRemBertForMultipleChoice.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</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.TFRemBertForMultipleChoice.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</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.TFRemBertForMultipleChoice.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</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.TFRemBertForMultipleChoice.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <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.TFRemBertForMultipleChoice.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.TFRemBertForMultipleChoice.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.TFRemBertForMultipleChoice.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
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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.TFRemBertForMultipleChoice.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> or <code>np.ndarray</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>
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<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/rembert#transformers.RemBertConfig"
>RemBertConfig</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>
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Initializing with a config file does not load the weights associated with the model, only the
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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.TFRemBertForTokenClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</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.TFRemBertForTokenClassification.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</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.TFRemBertForTokenClassification.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</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.TFRemBertForTokenClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</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.TFRemBertForTokenClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <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.TFRemBertForTokenClassification.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.TFRemBertForTokenClassification.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.TFRemBertForTokenClassification.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.TFRemBertForTokenClassification.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#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.TFRemBertForTokenClassification.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 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/rembert/modeling_tf_rembert.py#L1469",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/rembert#transformers.RemBertConfig"
>RemBertConfig</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>
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Initializing with a config file does not load the weights associated with the model, only the
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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.TFRemBertForQuestionAnswering.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</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.TFRemBertForQuestionAnswering.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</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.TFRemBertForQuestionAnswering.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</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.TFRemBertForQuestionAnswering.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</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.TFRemBertForQuestionAnswering.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <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.TFRemBertForQuestionAnswering.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.TFRemBertForQuestionAnswering.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.TFRemBertForQuestionAnswering.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.TFRemBertForQuestionAnswering.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
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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.TFRemBertForQuestionAnswering.call.end_positions",description:`<strong>end_positions</strong> (<code>tf.Tensor</code> or <code>np.ndarray</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/rembert/modeling_tf_rembert.py#L1547",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/rembert#transformers.RemBertConfig"
>RemBertConfig</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>
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significantly improve the efficiency of parameter allocation in the input embedding of multilingual models. By
reallocating the input embedding parameters in the Transformer layers, we achieve dramatically better performance on
standard natural language understanding tasks with the same number of parameters during fine-tuning. We also show that
allocating additional capacity to the output embedding provides benefits to the model that persist through the
fine-tuning stage even though the output embedding is discarded after pre-training. Our analysis shows that larger
output embeddings prevent the model\u2019s last layers from overspecializing to the pre-training task and encourage
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embeddings (preserved during fine-tuning) and bigger output embeddings (discarded at fine-tuning). The tokenizer is
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`),Fs=s("a"),_m=o("Unigram"),km=o(`. This
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Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
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Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
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`),xl=s("code"),m_=o("decoder_input_ids"),h_=o(" of shape "),jl=s("code"),u_=o("(batch_size, sequence_length)"),f_=o(`.
use_cache (`),Al=s("code"),g_=o("bool"),__=o(", "),Pl=s("em"),k_=o("optional"),b_=o(", defaults to "),Ll=s("code"),v_=o("True"),y_=o(`):
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standard natural language understanding tasks with the same number of parameters during fine-tuning. We also show that
allocating additional capacity to the output embedding provides benefits to the model that persist through the
fine-tuning stage even though the output embedding is discarded after pre-training. Our analysis shows that larger
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This model is a PyTorch `),Is=r(Qa,"A",{href:!0,rel:!0});var $v=a(Is);Rh=n($v,"torch.nn.Module"),$v.forEach(t),Bh=n(Qa,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Qa.forEach(t),Mh=m(Ha),zt=r(Ha,"DIV",{class:!0});var es=a(zt);w(Ss.$$.fragment,es),zh=m(es),uo=r(es,"P",{});var Va=a(uo);Ch=n(Va,"The "),wa=r(Va,"A",{href:!0});var Fv=a(wa);qh=n(Fv,"RemBertForCausalLM"),Fv.forEach(t),xh=n(Va," forward method, overrides the "),Ni=r(Va,"CODE",{});var Ev=a(Ni);jh=n(Ev,"__call__"),Ev.forEach(t),Ah=n(Va," special method."),Va.forEach(t),Ph=m(es),w(en.$$.fragment,es),Lh=m(es),w(tn.$$.fragment,es),es.forEach(t),Ha.forEach(t),Yd=m(i),fo=r(i,"H2",{class:!0});var Lc=a(fo);on=r(Lc,"A",{id:!0,class:!0,href:!0});var Rv=a(on);Ii=r(Rv,"SPAN",{});var Bv=a(Ii);w(Ws.$$.fragment,Bv),Bv.forEach(t),Rv.forEach(t),Oh=m(Lc),Si=r(Lc,"SPAN",{});var Mv=a(Si);Dh=n(Mv,"RemBertForMaskedLM"),Mv.forEach(t),Lc.forEach(t),Zd=m(i),Qt=r(i,"DIV",{class:!0});var Ja=a(Qt);w(Us.$$.fragment,Ja),Nh=m(Ja),go=r(Ja,"P",{});var Ga=a(go);Ih=n(Ga,"RemBERT Model with a "),Wi=r(Ga,"CODE",{});var zv=a(Wi);Sh=n(zv,"language modeling"),zv.forEach(t),Wh=n(Ga,` head on top.
This model is a PyTorch `),Ks=r(Ga,"A",{href:!0,rel:!0});var Cv=a(Ks);Uh=n(Cv,"torch.nn.Module"),Cv.forEach(t),Kh=n(Ga,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Ga.forEach(t),Hh=m(Ja),Ct=r(Ja,"DIV",{class:!0});var ts=a(Ct);w(Hs.$$.fragment,ts),Qh=m(ts),_o=r(ts,"P",{});var Xa=a(_o);Vh=n(Xa,"The "),$a=r(Xa,"A",{href:!0});var qv=a($a);Jh=n(qv,"RemBertForMaskedLM"),qv.forEach(t),Gh=n(Xa," forward method, overrides the "),Ui=r(Xa,"CODE",{});var xv=a(Ui);Xh=n(xv,"__call__"),xv.forEach(t),Yh=n(Xa," special method."),Xa.forEach(t),Zh=m(ts),w(nn.$$.fragment,ts),eu=m(ts),w(sn.$$.fragment,ts),ts.forEach(t),Ja.forEach(t),ec=m(i),ko=r(i,"H2",{class:!0});var Oc=a(ko);rn=r(Oc,"A",{id:!0,class:!0,href:!0});var jv=a(rn);Ki=r(jv,"SPAN",{});var Av=a(Ki);w(Qs.$$.fragment,Av),Av.forEach(t),jv.forEach(t),tu=m(Oc),Hi=r(Oc,"SPAN",{});var Pv=a(Hi);ou=n(Pv,"RemBertForSequenceClassification"),Pv.forEach(t),Oc.forEach(t),tc=m(i),wt=r(i,"DIV",{class:!0});var os=a(wt);w(Vs.$$.fragment,os),nu=m(os),Qi=r(os,"P",{});var Lv=a(Qi);su=n(Lv,`RemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`),Lv.forEach(t),ru=m(os),Js=r(os,"P",{});var Dc=a(Js);au=n(Dc,"This model is a PyTorch "),Gs=r(Dc,"A",{href:!0,rel:!0});var Ov=a(Gs);iu=n(Ov,"torch.nn.Module"),Ov.forEach(t),lu=n(Dc,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Dc.forEach(t),du=m(os),ft=r(os,"DIV",{class:!0});var Yt=a(ft);w(Xs.$$.fragment,Yt),cu=m(Yt),bo=r(Yt,"P",{});var Ya=a(bo);pu=n(Ya,"The "),Fa=r(Ya,"A",{href:!0});var Dv=a(Fa);mu=n(Dv,"RemBertForSequenceClassification"),Dv.forEach(t),hu=n(Ya," forward method, overrides the "),Vi=r(Ya,"CODE",{});var Nv=a(Vi);uu=n(Nv,"__call__"),Nv.forEach(t),fu=n(Ya," special method."),Ya.forEach(t),gu=m(Yt),w(an.$$.fragment,Yt),_u=m(Yt),w(ln.$$.fragment,Yt),ku=m(Yt),w(dn.$$.fragment,Yt),Yt.forEach(t),os.forEach(t),oc=m(i),vo=r(i,"H2",{class:!0});var Nc=a(vo);cn=r(Nc,"A",{id:!0,class:!0,href:!0});var Iv=a(cn);Ji=r(Iv,"SPAN",{});var Sv=a(Ji);w(Ys.$$.fragment,Sv),Sv.forEach(t),Iv.forEach(t),bu=m(Nc),Gi=r(Nc,"SPAN",{});var Wv=a(Gi);vu=n(Wv,"RemBertForMultipleChoice"),Wv.forEach(t),Nc.forEach(t),nc=m(i),$t=r(i,"DIV",{class:!0});var ns=a($t);w(Zs.$$.fragment,ns),yu=m(ns),Xi=r(ns,"P",{});var Uv=a(Xi);Tu=n(Uv,`RemBERT 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.`),Uv.forEach(t),wu=m(ns),er=r(ns,"P",{});var Ic=a(er);$u=n(Ic,"This model is a PyTorch "),tr=r(Ic,"A",{href:!0,rel:!0});var Kv=a(tr);Fu=n(Kv,"torch.nn.Module"),Kv.forEach(t),Eu=n(Ic,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Ic.forEach(t),Ru=m(ns),qt=r(ns,"DIV",{class:!0});var ss=a(qt);w(or.$$.fragment,ss),Bu=m(ss),yo=r(ss,"P",{});var Za=a(yo);Mu=n(Za,"The "),Ea=r(Za,"A",{href:!0});var Hv=a(Ea);zu=n(Hv,"RemBertForMultipleChoice"),Hv.forEach(t),Cu=n(Za," forward method, overrides the "),Yi=r(Za,"CODE",{});var Qv=a(Yi);qu=n(Qv,"__call__"),Qv.forEach(t),xu=n(Za," special method."),Za.forEach(t),ju=m(ss),w(pn.$$.fragment,ss),Au=m(ss),w(mn.$$.fragment,ss),ss.forEach(t),ns.forEach(t),sc=m(i),To=r(i,"H2",{class:!0});var Sc=a(To);hn=r(Sc,"A",{id:!0,class:!0,href:!0});var Vv=a(hn);Zi=r(Vv,"SPAN",{});var Jv=a(Zi);w(nr.$$.fragment,Jv),Jv.forEach(t),Vv.forEach(t),Pu=m(Sc),el=r(Sc,"SPAN",{});var Gv=a(el);Lu=n(Gv,"RemBertForTokenClassification"),Gv.forEach(t),Sc.forEach(t),rc=m(i),Ft=r(i,"DIV",{class:!0});var rs=a(Ft);w(sr.$$.fragment,rs),Ou=m(rs),tl=r(rs,"P",{});var Xv=a(tl);Du=n(Xv,`RemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.`),Xv.forEach(t),Nu=m(rs),rr=r(rs,"P",{});var Wc=a(rr);Iu=n(Wc,"This model is a PyTorch "),ar=r(Wc,"A",{href:!0,rel:!0});var Yv=a(ar);Su=n(Yv,"torch.nn.Module"),Yv.forEach(t),Wu=n(Wc,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Wc.forEach(t),Uu=m(rs),xt=r(rs,"DIV",{class:!0});var as=a(xt);w(ir.$$.fragment,as),Ku=m(as),wo=r(as,"P",{});var ei=a(wo);Hu=n(ei,"The "),Ra=r(ei,"A",{href:!0});var Zv=a(Ra);Qu=n(Zv,"RemBertForTokenClassification"),Zv.forEach(t),Vu=n(ei," forward method, overrides the "),ol=r(ei,"CODE",{});var ey=a(ol);Ju=n(ey,"__call__"),ey.forEach(t),Gu=n(ei," special method."),ei.forEach(t),Xu=m(as),w(un.$$.fragment,as),Yu=m(as),w(fn.$$.fragment,as),as.forEach(t),rs.forEach(t),ac=m(i),$o=r(i,"H2",{class:!0});var Uc=a($o);gn=r(Uc,"A",{id:!0,class:!0,href:!0});var ty=a(gn);nl=r(ty,"SPAN",{});var oy=a(nl);w(lr.$$.fragment,oy),oy.forEach(t),ty.forEach(t),Zu=m(Uc),sl=r(Uc,"SPAN",{});var ny=a(sl);ef=n(ny,"RemBertForQuestionAnswering"),ny.forEach(t),Uc.forEach(t),ic=m(i),Et=r(i,"DIV",{class:!0});var is=a(Et);w(dr.$$.fragment,is),tf=m(is),Fo=r(is,"P",{});var ti=a(Fo);of=n(ti,`RemBERT 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 `),rl=r(ti,"CODE",{});var sy=a(rl);nf=n(sy,"span start logits"),sy.forEach(t),sf=n(ti," and "),al=r(ti,"CODE",{});var ry=a(al);rf=n(ry,"span end logits"),ry.forEach(t),af=n(ti,")."),ti.forEach(t),lf=m(is),cr=r(is,"P",{});var Kc=a(cr);df=n(Kc,"This model is a PyTorch "),pr=r(Kc,"A",{href:!0,rel:!0});var ay=a(pr);cf=n(ay,"torch.nn.Module"),ay.forEach(t),pf=n(Kc,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Kc.forEach(t),mf=m(is),jt=r(is,"DIV",{class:!0});var ls=a(jt);w(mr.$$.fragment,ls),hf=m(ls),Eo=r(ls,"P",{});var oi=a(Eo);uf=n(oi,"The "),Ba=r(oi,"A",{href:!0});var iy=a(Ba);ff=n(iy,"RemBertForQuestionAnswering"),iy.forEach(t),gf=n(oi," forward method, overrides the "),il=r(oi,"CODE",{});var ly=a(il);_f=n(ly,"__call__"),ly.forEach(t),kf=n(oi," special method."),oi.forEach(t),bf=m(ls),w(_n.$$.fragment,ls),vf=m(ls),w(kn.$$.fragment,ls),ls.forEach(t),is.forEach(t),lc=m(i),Ro=r(i,"H2",{class:!0});var Hc=a(Ro);bn=r(Hc,"A",{id:!0,class:!0,href:!0});var dy=a(bn);ll=r(dy,"SPAN",{});var cy=a(ll);w(hr.$$.fragment,cy),cy.forEach(t),dy.forEach(t),yf=m(Hc),dl=r(Hc,"SPAN",{});var py=a(dl);Tf=n(py,"TFRemBertModel"),py.forEach(t),Hc.forEach(t),dc=m(i),rt=r(i,"DIV",{class:!0});var Ot=a(rt);w(ur.$$.fragment,Ot),wf=m(Ot),cl=r(Ot,"P",{});var my=a(cl);$f=n(my,"The bare RemBERT Model transformer outputing raw hidden-states without any specific head on top."),my.forEach(t),Ff=m(Ot),fr=r(Ot,"P",{});var Qc=a(fr);Ef=n(Qc,"This model inherits from "),Ma=r(Qc,"A",{href:!0});var hy=a(Ma);Rf=n(hy,"TFPreTrainedModel"),hy.forEach(t),Bf=n(Qc,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Qc.forEach(t),Mf=m(Ot),gr=r(Ot,"P",{});var Vc=a(gr);zf=n(Vc,"This model is also a "),_r=r(Vc,"A",{href:!0,rel:!0});var uy=a(_r);Cf=n(uy,"tf.keras.Model"),uy.forEach(t),qf=n(Vc,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),Vc.forEach(t),xf=m(Ot),w(vn.$$.fragment,Ot),jf=m(Ot),At=r(Ot,"DIV",{class:!0});var ds=a(At);w(kr.$$.fragment,ds),Af=m(ds),Bo=r(ds,"P",{});var ni=a(Bo);Pf=n(ni,"The "),za=r(ni,"A",{href:!0});var fy=a(za);Lf=n(fy,"TFRemBertModel"),fy.forEach(t),Of=n(ni," forward method, overrides the "),pl=r(ni,"CODE",{});var gy=a(pl);Df=n(gy,"__call__"),gy.forEach(t),Nf=n(ni," special method."),ni.forEach(t),If=m(ds),w(yn.$$.fragment,ds),Sf=m(ds),w(Tn.$$.fragment,ds),ds.forEach(t),Ot.forEach(t),cc=m(i),Mo=r(i,"H2",{class:!0});var Jc=a(Mo);wn=r(Jc,"A",{id:!0,class:!0,href:!0});var _y=a(wn);ml=r(_y,"SPAN",{});var ky=a(ml);w(br.$$.fragment,ky),ky.forEach(t),_y.forEach(t),Wf=m(Jc),hl=r(Jc,"SPAN",{});var by=a(hl);Uf=n(by,"TFRemBertForMaskedLM"),by.forEach(t),Jc.forEach(t),pc=m(i),at=r(i,"DIV",{class:!0});var Dt=a(at);w(vr.$$.fragment,Dt),Kf=m(Dt),yr=r(Dt,"P",{});var Gc=a(yr);Hf=n(Gc,"RemBERT Model with a "),ul=r(Gc,"CODE",{});var vy=a(ul);Qf=n(vy,"language modeling"),vy.forEach(t),Vf=n(Gc," head on top."),Gc.forEach(t),Jf=m(Dt),Tr=r(Dt,"P",{});var Xc=a(Tr);Gf=n(Xc,"This model inherits from "),Ca=r(Xc,"A",{href:!0});var yy=a(Ca);Xf=n(yy,"TFPreTrainedModel"),yy.forEach(t),Yf=n(Xc,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Xc.forEach(t),Zf=m(Dt),wr=r(Dt,"P",{});var Yc=a(wr);eg=n(Yc,"This model is also a "),$r=r(Yc,"A",{href:!0,rel:!0});var Ty=a($r);tg=n(Ty,"tf.keras.Model"),Ty.forEach(t),og=n(Yc,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),Yc.forEach(t),ng=m(Dt),w($n.$$.fragment,Dt),sg=m(Dt),gt=r(Dt,"DIV",{class:!0});var Zt=a(gt);w(Fr.$$.fragment,Zt),rg=m(Zt),zo=r(Zt,"P",{});var si=a(zo);ag=n(si,"The "),qa=r(si,"A",{href:!0});var wy=a(qa);ig=n(wy,"TFRemBertForMaskedLM"),wy.forEach(t),lg=n(si," forward method, overrides the "),fl=r(si,"CODE",{});var $y=a(fl);dg=n($y,"__call__"),$y.forEach(t),cg=n(si," special method."),si.forEach(t),pg=m(Zt),w(Fn.$$.fragment,Zt),mg=m(Zt),w(En.$$.fragment,Zt),hg=m(Zt),w(Rn.$$.fragment,Zt),Zt.forEach(t),Dt.forEach(t),mc=m(i),Co=r(i,"H2",{class:!0});var Zc=a(Co);Bn=r(Zc,"A",{id:!0,class:!0,href:!0});var Fy=a(Bn);gl=r(Fy,"SPAN",{});var Ey=a(gl);w(Er.$$.fragment,Ey),Ey.forEach(t),Fy.forEach(t),ug=m(Zc),_l=r(Zc,"SPAN",{});var Ry=a(_l);fg=n(Ry,"TFRemBertForCausalLM"),Ry.forEach(t),Zc.forEach(t),hc=m(i),it=r(i,"DIV",{class:!0});var Nt=a(it);w(Rr.$$.fragment,Nt),gg=m(Nt),Br=r(Nt,"P",{});var ep=a(Br);_g=n(ep,"RemBERT Model with a "),kl=r(ep,"CODE",{});var By=a(kl);kg=n(By,"language modeling"),By.forEach(t),bg=n(ep," head on top for CLM fine-tuning."),ep.forEach(t),vg=m(Nt),Mr=r(Nt,"P",{});var tp=a(Mr);yg=n(tp,"This model inherits from "),xa=r(tp,"A",{href:!0});var My=a(xa);Tg=n(My,"TFPreTrainedModel"),My.forEach(t),wg=n(tp,`. 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.)`),tp.forEach(t),$g=m(Nt),zr=r(Nt,"P",{});var op=a(zr);Fg=n(op,"This model is also a "),Cr=r(op,"A",{href:!0,rel:!0});var zy=a(Cr);Eg=n(zy,"tf.keras.Model"),zy.forEach(t),Rg=n(op,` 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.`),op.forEach(t),Bg=m(Nt),w(Mn.$$.fragment,Nt),Mg=m(Nt),_t=r(Nt,"DIV",{class:!0});var eo=a(_t);w(qr.$$.fragment,eo),zg=m(eo),ot=r(eo,"P",{});var mt=a(ot);Cg=n(mt,"encoder_hidden_states  ("),bl=r(mt,"CODE",{});var Cy=a(bl);qg=n(Cy,"tf.Tensor"),Cy.forEach(t),xg=n(mt," of shape "),vl=r(mt,"CODE",{});var qy=a(vl);jg=n(qy,"(batch_size, sequence_length, hidden_size)"),qy.forEach(t),Ag=n(mt,", "),yl=r(mt,"EM",{});var xy=a(yl);Pg=n(xy,"optional"),xy.forEach(t),Lg=n(mt,`):
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.
encoder_attention_mask (`),Tl=r(mt,"CODE",{});var jy=a(Tl);Og=n(jy,"tf.Tensor"),jy.forEach(t),Dg=n(mt," of shape "),wl=r(mt,"CODE",{});var Ay=a(wl);Ng=n(Ay,"(batch_size, sequence_length)"),Ay.forEach(t),Ig=n(mt,", "),$l=r(mt,"EM",{});var Py=a($l);Sg=n(Py,"optional"),Py.forEach(t),Wg=n(mt,`):
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 `),Fl=r(mt,"CODE",{});var Ly=a(Fl);Ug=n(Ly,"[0, 1]"),Ly.forEach(t),Kg=n(mt,":"),mt.forEach(t),Hg=m(eo),xr=r(eo,"UL",{});var np=a(xr);jr=r(np,"LI",{});var sp=a(jr);Qg=n(sp,"1 for tokens that are "),El=r(sp,"STRONG",{});var Oy=a(El);Vg=n(Oy,"not masked"),Oy.forEach(t),Jg=n(sp,","),sp.forEach(t),Gg=m(np),Ar=r(np,"LI",{});var rp=a(Ar);Xg=n(rp,"0 for tokens that are "),Rl=r(rp,"STRONG",{});var Dy=a(Rl);Yg=n(Dy,"masked"),Dy.forEach(t),Zg=n(rp,"."),rp.forEach(t),np.forEach(t),e_=m(eo),Y=r(eo,"P",{});var ee=a(Y);t_=n(ee,"past_key_values ("),Bl=r(ee,"CODE",{});var Ny=a(Bl);o_=n(Ny,"Tuple[Tuple[tf.Tensor]]"),Ny.forEach(t),n_=n(ee," of length "),Ml=r(ee,"CODE",{});var Iy=a(Ml);s_=n(Iy,"config.n_layers"),Iy.forEach(t),r_=n(ee,`)
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `),zl=r(ee,"CODE",{});var Sy=a(zl);a_=n(Sy,"past_key_values"),Sy.forEach(t),i_=n(ee," are used, the user can optionally input only the last "),Cl=r(ee,"CODE",{});var Wy=a(Cl);l_=n(Wy,"decoder_input_ids"),Wy.forEach(t),d_=n(ee,` (those that
don\u2019t have their past key value states given to this model) of shape `),ql=r(ee,"CODE",{});var Uy=a(ql);c_=n(Uy,"(batch_size, 1)"),Uy.forEach(t),p_=n(ee,` instead of all
`),xl=r(ee,"CODE",{});var Ky=a(xl);m_=n(Ky,"decoder_input_ids"),Ky.forEach(t),h_=n(ee," of shape "),jl=r(ee,"CODE",{});var Hy=a(jl);u_=n(Hy,"(batch_size, sequence_length)"),Hy.forEach(t),f_=n(ee,`.
use_cache (`),Al=r(ee,"CODE",{});var Qy=a(Al);g_=n(Qy,"bool"),Qy.forEach(t),__=n(ee,", "),Pl=r(ee,"EM",{});var Vy=a(Pl);k_=n(Vy,"optional"),Vy.forEach(t),b_=n(ee,", defaults to "),Ll=r(ee,"CODE",{});var Jy=a(Ll);v_=n(Jy,"True"),Jy.forEach(t),y_=n(ee,`):
If set to `),Ol=r(ee,"CODE",{});var Gy=a(Ol);T_=n(Gy,"True"),Gy.forEach(t),w_=n(ee,", "),Dl=r(ee,"CODE",{});var Xy=a(Dl);$_=n(Xy,"past_key_values"),Xy.forEach(t),F_=n(ee,` key value states are returned and can be used to speed up decoding (see
`),Nl=r(ee,"CODE",{});var Yy=a(Nl);E_=n(Yy,"past_key_values"),Yy.forEach(t),R_=n(ee,"). Set to "),Il=r(ee,"CODE",{});var Zy=a(Il);B_=n(Zy,"False"),Zy.forEach(t),M_=n(ee," during training, "),Sl=r(ee,"CODE",{});var eT=a(Sl);z_=n(eT,"True"),eT.forEach(t),C_=n(ee,` during generation
labels (`),Wl=r(ee,"CODE",{});var tT=a(Wl);q_=n(tT,"tf.Tensor"),tT.forEach(t),x_=n(ee," or "),Ul=r(ee,"CODE",{});var oT=a(Ul);j_=n(oT,"np.ndarray"),oT.forEach(t),A_=n(ee," of shape "),Kl=r(ee,"CODE",{});var nT=a(Kl);P_=n(nT,"(batch_size, sequence_length)"),nT.forEach(t),L_=n(ee,", "),Hl=r(ee,"EM",{});var sT=a(Hl);O_=n(sT,"optional"),sT.forEach(t),D_=n(ee,`):
Labels for computing the cross entropy classification loss. Indices should be in `),Ql=r(ee,"CODE",{});var rT=a(Ql);N_=n(rT,"[0, ..., config.vocab_size - 1]"),rT.forEach(t),I_=n(ee,"."),ee.forEach(t),S_=m(eo),w(zn.$$.fragment,eo),eo.forEach(t),Nt.forEach(t),uc=m(i),qo=r(i,"H2",{class:!0});var ap=a(qo);Cn=r(ap,"A",{id:!0,class:!0,href:!0});var aT=a(Cn);Vl=r(aT,"SPAN",{});var iT=a(Vl);w(Pr.$$.fragment,iT),iT.forEach(t),aT.forEach(t),W_=m(ap),Jl=r(ap,"SPAN",{});var lT=a(Jl);U_=n(lT,"TFRemBertForSequenceClassification"),lT.forEach(t),ap.forEach(t),fc=m(i),lt=r(i,"DIV",{class:!0});var It=a(lt);w(Lr.$$.fragment,It),K_=m(It),Gl=r(It,"P",{});var dT=a(Gl);H_=n(dT,"RemBERT Model transformer with a sequence classification/regression head on top e.g., for GLUE tasks."),dT.forEach(t),Q_=m(It),Or=r(It,"P",{});var ip=a(Or);V_=n(ip,"This model inherits from "),ja=r(ip,"A",{href:!0});var cT=a(ja);J_=n(cT,"TFPreTrainedModel"),cT.forEach(t),G_=n(ip,`. 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
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