import{S as E2,i as B2,s as x2,e as r,k as p,w as y,t as o,M as z2,c as a,d as t,m as h,a as i,x as w,h as n,b as g,G as e,g as _,y as $,q as M,o as F,B as E,v as q2,L as Je}from"../../chunks/vendor-hf-doc-builder.js";import{T as Ve}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 Xe}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Ge}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function P2(q){let d,v,m,u,k;return u=new Ye({props:{code:`from transformers import MobileBertConfig, MobileBertModel

# Initializing a MobileBERT configuration
configuration = MobileBertConfig()

# Initializing a model (with random weights) from the configuration above
model = MobileBertModel(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> MobileBertConfig, MobileBertModel

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){d=r("p"),v=o("Examples:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Examples:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function j2(q){let d,v,m,u,k;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=r("p"),v=o("sequence pair mask has the following format:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"sequence pair mask has the following format:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function C2(q){let d,v,m,u,k;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=r("p"),v=o("sequence pair mask has the following format:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"sequence pair mask has the following format:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function N2(q){let d,v,m,u,k;return{c(){d=r("p"),v=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),u=o("Module"),k=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function A2(q){let d,v,m,u,k;return u=new Ye({props:{code:`from transformers import AutoTokenizer, MobileBertModel
import torch

tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
model = MobileBertModel.from_pretrained("google/mobilebert-uncased")

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, MobileBertModel
<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/mobilebert-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileBertModel.from_pretrained(<span class="hljs-string">&quot;google/mobilebert-uncased&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){d=r("p"),v=o("Example:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Example:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function O2(q){let d,v,m,u,k;return{c(){d=r("p"),v=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),u=o("Module"),k=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function D2(q){let d,v,m,u,k;return u=new Ye({props:{code:`from transformers import AutoTokenizer, MobileBertForPreTraining
import torch

tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
model = MobileBertForPreTraining.from_pretrained("google/mobilebert-uncased")

input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)
# Batch size 1
outputs = model(input_ids)

prediction_logits = outputs.prediction_logits
seq_relationship_logits = outputs.seq_relationship_logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, MobileBertForPreTraining
<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/mobilebert-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileBertForPreTraining.from_pretrained(<span class="hljs-string">&quot;google/mobilebert-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = torch.tensor(tokenizer.encode(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, add_special_tokens=<span class="hljs-literal">True</span>)).unsqueeze(<span class="hljs-number">0</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids)

<span class="hljs-meta">&gt;&gt;&gt; </span>prediction_logits = outputs.prediction_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>seq_relationship_logits = outputs.seq_relationship_logits`}}),{c(){d=r("p"),v=o("Examples:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Examples:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function S2(q){let d,v,m,u,k;return{c(){d=r("p"),v=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),u=o("Module"),k=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function I2(q){let d,v,m,u,k;return u=new Ye({props:{code:`from transformers import AutoTokenizer, MobileBertForMaskedLM
import torch

tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
model = MobileBertForMaskedLM.from_pretrained("google/mobilebert-uncased")

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

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

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

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

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

outputs = model(**inputs, labels=labels)
round(outputs.loss.item(), 2)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, MobileBertForMaskedLM
<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/mobilebert-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileBertForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google/mobilebert-uncased&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>tokenizer.decode(predicted_token_id)
<span class="hljs-string">&#x27;paris&#x27;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tokenizer(<span class="hljs-string">&quot;The capital of France is Paris.&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># mask labels of non-[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)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(outputs.loss.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">0.57</span>`}}),{c(){d=r("p"),v=o("Example:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Example:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function L2(q){let d,v,m,u,k;return{c(){d=r("p"),v=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),u=o("Module"),k=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function W2(q){let d,v,m,u,k;return u=new Ye({props:{code:`from transformers import AutoTokenizer, MobileBertForNextSentencePrediction
import torch

tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
model = MobileBertForNextSentencePrediction.from_pretrained("google/mobilebert-uncased")

prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
next_sentence = "The sky is blue due to the shorter wavelength of blue light."
encoding = tokenizer(prompt, next_sentence, return_tensors="pt")

outputs = model(**encoding, labels=torch.LongTensor([1]))
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, MobileBertForNextSentencePrediction
<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/mobilebert-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileBertForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;google/mobilebert-uncased&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>next_sentence = <span class="hljs-string">&quot;The sky is blue due to the shorter wavelength of blue light.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = tokenizer(prompt, next_sentence, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**encoding, labels=torch.LongTensor([<span class="hljs-number">1</span>]))
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=r("p"),v=o("Examples:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Examples:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function R2(q){let d,v,m,u,k;return{c(){d=r("p"),v=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),u=o("Module"),k=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function U2(q){let d,v,m,u,k;return u=new Ye({props:{code:`import torch
from transformers import AutoTokenizer, MobileBertForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("lordtt13/emo-mobilebert")
model = MobileBertForSequenceClassification.from_pretrained("lordtt13/emo-mobilebert")

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

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

predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]

# 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 = MobileBertForSequenceClassification.from_pretrained("lordtt13/emo-mobilebert", num_labels=num_labels)

labels = torch.tensor([1])
loss = model(**inputs, labels=labels).loss
round(loss.item(), 2)`,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, MobileBertForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;lordtt13/emo-mobilebert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;lordtt13/emo-mobilebert&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>model.config.id2label[predicted_class_id]
<span class="hljs-string">&#x27;others&#x27;</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 = MobileBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;lordtt13/emo-mobilebert&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
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(loss.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">4.72</span>`}}),{c(){d=r("p"),v=o("Example of single-label classification:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Example of single-label classification:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function K2(q){let d,v,m,u,k;return u=new Ye({props:{code:`import torch
from transformers import AutoTokenizer, MobileBertForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("lordtt13/emo-mobilebert")
model = MobileBertForSequenceClassification.from_pretrained("lordtt13/emo-mobilebert", 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 = MobileBertForSequenceClassification.from_pretrained(
    "lordtt13/emo-mobilebert", 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, MobileBertForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;lordtt13/emo-mobilebert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;lordtt13/emo-mobilebert&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 = MobileBertForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;lordtt13/emo-mobilebert&quot;</span>, num_labels=num_labels, problem_type=<span class="hljs-string">&quot;multi_label_classification&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = torch.<span class="hljs-built_in">sum</span>(
<span class="hljs-meta">... </span>    torch.nn.functional.one_hot(predicted_class_ids[<span class="hljs-literal">None</span>, :].clone(), num_classes=num_labels), dim=<span class="hljs-number">1</span>
<span class="hljs-meta">... </span>).to(torch.<span class="hljs-built_in">float</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss`}}),{c(){d=r("p"),v=o("Example of multi-label classification:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Example of multi-label classification:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function H2(q){let d,v,m,u,k;return{c(){d=r("p"),v=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),u=o("Module"),k=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function Q2(q){let d,v,m,u,k;return u=new Ye({props:{code:`from transformers import AutoTokenizer, MobileBertForMultipleChoice
import torch

tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
model = MobileBertForMultipleChoice.from_pretrained("google/mobilebert-uncased")

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, MobileBertForMultipleChoice
<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/mobilebert-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileBertForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google/mobilebert-uncased&quot;</span>)

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># the linear classifier still needs to be trained</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=r("p"),v=o("Example:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Example:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function V2(q){let d,v,m,u,k;return{c(){d=r("p"),v=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),u=o("Module"),k=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function G2(q){let d,v,m,u,k;return u=new Ye({props:{code:`from transformers import AutoTokenizer, MobileBertForTokenClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("mrm8488/mobilebert-finetuned-ner")
model = MobileBertForTokenClassification.from_pretrained("mrm8488/mobilebert-finetuned-ner")

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]]
predicted_tokens_classes

labels = predicted_token_class_ids
loss = model(**inputs, labels=labels).loss
round(loss.item(), 2)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, MobileBertForTokenClassification
<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;mrm8488/mobilebert-finetuned-ner&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileBertForTokenClassification.from_pretrained(<span class="hljs-string">&quot;mrm8488/mobilebert-finetuned-ner&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>predicted_tokens_classes
[<span class="hljs-string">&#x27;I-ORG&#x27;</span>, <span class="hljs-string">&#x27;I-ORG&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>, <span class="hljs-string">&#x27;I-LOC&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>, <span class="hljs-string">&#x27;I-LOC&#x27;</span>, <span class="hljs-string">&#x27;I-LOC&#x27;</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
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(loss.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">0.03</span>`}}),{c(){d=r("p"),v=o("Example:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Example:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function J2(q){let d,v,m,u,k;return{c(){d=r("p"),v=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),u=o("Module"),k=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function Y2(q){let d,v,m,u,k;return u=new Ye({props:{code:`from transformers import AutoTokenizer, MobileBertForQuestionAnswering
import torch

tokenizer = AutoTokenizer.from_pretrained("csarron/mobilebert-uncased-squad-v2")
model = MobileBertForQuestionAnswering.from_pretrained("csarron/mobilebert-uncased-squad-v2")

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]
tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)

# target is "nice puppet"
target_start_index = torch.tensor([12])
target_end_index = torch.tensor([13])

outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
loss = outputs.loss
round(loss.item(), 2)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, MobileBertForQuestionAnswering
<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;csarron/mobilebert-uncased-squad-v2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileBertForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;csarron/mobilebert-uncased-squad-v2&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>tokenizer.decode(predict_answer_tokens, skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-string">&#x27;a nice puppet&#x27;</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">12</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>target_end_index = torch.tensor([<span class="hljs-number">13</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
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(loss.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">3.98</span>`}}),{c(){d=r("p"),v=o("Example:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Example:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function X2(q){let d,v,m,u,k,l,c,z,ve,me,S,re,Z,T,Te,K,ye,ue,L,we,ie,Q,$e,fe,R,Me,ge,U,ce,Fe,te,P,N,_e,W,pe,Ee,V,he,Be,C,se,G,le,xe,J,de,ze,O,be,I,ae,ee,j,qe,A,Pe,ke;return{c(){d=r("p"),v=o("TensorFlow models and layers in "),m=r("code"),u=o("transformers"),k=o(" accept two formats as input:"),l=p(),c=r("ul"),z=r("li"),ve=o("having all inputs as keyword arguments (like PyTorch models), or"),me=p(),S=r("li"),re=o("having all inputs as a list, tuple or dict in the first positional argument."),Z=p(),T=r("p"),Te=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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format outside of Keras methods like `),Q=r("code"),$e=o("fit()"),fe=o(" and "),R=r("code"),Me=o("predict()"),ge=o(`, such as when creating your own layers or models with
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`),I=a(je,"CODE",{});var We=i(I);ae=n(We,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),We.forEach(t),je.forEach(t),D.forEach(t),ee=h(f),j=a(f,"P",{});var X=i(j);qe=n(X,`Note that when creating models and layers with
`),A=a(X,"A",{href:!0,rel:!0});var Ce=i(A);Pe=n(Ce,"subclassing"),Ce.forEach(t),ke=n(X,` 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!`),X.forEach(t),this.h()},h(){g(A,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),g(A,"rel","nofollow")},m(f,B){_(f,d,B),e(d,v),e(d,m),e(m,u),e(d,k),_(f,l,B),_(f,c,B),e(c,z),e(z,ve),e(c,me),e(c,S),e(S,re),_(f,Z,B),_(f,T,B),e(T,Te),e(T,K),e(K,ye),e(T,ue),e(T,L),e(L,we),e(T,ie),e(T,Q),e(Q,$e),e(T,fe),e(T,R),e(R,Me),e(T,ge),e(T,U),e(U,ce),e(T,Fe),_(f,te,B),_(f,P,B),e(P,N),e(N,_e),e(N,W),e(W,pe),e(N,Ee),e(N,V),e(V,he),e(P,Be),e(P,C),e(C,se),e(C,G),e(G,le),e(C,xe),e(C,J),e(J,de),e(P,ze),e(P,O),e(O,be),e(O,I),e(I,ae),_(f,ee,B),_(f,j,B),e(j,qe),e(j,A),e(A,Pe),e(j,ke)},d(f){f&&t(d),f&&t(l),f&&t(c),f&&t(Z),f&&t(T),f&&t(te),f&&t(P),f&&t(ee),f&&t(j)}}}function Z2(q){let d,v,m,u,k;return{c(){d=r("p"),v=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),u=o("Module"),k=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function e1(q){let d,v,m,u,k;return u=new Ye({props:{code:`from transformers import AutoTokenizer, TFMobileBertModel
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
model = TFMobileBertModel.from_pretrained("google/mobilebert-uncased")

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, TFMobileBertModel
<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/mobilebert-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMobileBertModel.from_pretrained(<span class="hljs-string">&quot;google/mobilebert-uncased&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){d=r("p"),v=o("Example:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Example:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function t1(q){let d,v,m,u,k,l,c,z,ve,me,S,re,Z,T,Te,K,ye,ue,L,we,ie,Q,$e,fe,R,Me,ge,U,ce,Fe,te,P,N,_e,W,pe,Ee,V,he,Be,C,se,G,le,xe,J,de,ze,O,be,I,ae,ee,j,qe,A,Pe,ke;return{c(){d=r("p"),v=o("TensorFlow models and layers in "),m=r("code"),u=o("transformers"),k=o(" accept two formats as input:"),l=p(),c=r("ul"),z=r("li"),ve=o("having all inputs as keyword arguments (like PyTorch models), or"),me=p(),S=r("li"),re=o("having all inputs as a list, tuple or dict in the first positional argument."),Z=p(),T=r("p"),Te=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),K=r("code"),ye=o("model.fit()"),ue=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=r("code"),we=o("model.fit()"),ie=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),Q=r("code"),$e=o("fit()"),fe=o(" and "),R=r("code"),Me=o("predict()"),ge=o(`, such as when creating your own layers or models with
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positional argument:`),te=p(),P=r("ul"),N=r("li"),_e=o("a single Tensor with "),W=r("code"),pe=o("input_ids"),Ee=o(" only and nothing else: "),V=r("code"),he=o("model(input_ids)"),Be=p(),C=r("li"),se=o(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),G=r("code"),le=o("model([input_ids, attention_mask])"),xe=o(" or "),J=r("code"),de=o("model([input_ids, attention_mask, token_type_ids])"),ze=p(),O=r("li"),be=o(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),I=r("code"),ae=o('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),ee=p(),j=r("p"),qe=o(`Note that when creating models and layers with
`),A=r("a"),Pe=o("subclassing"),ke=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=a(f,"P",{});var B=i(d);v=n(B,"TensorFlow models and layers in "),m=a(B,"CODE",{});var Ne=i(m);u=n(Ne,"transformers"),Ne.forEach(t),k=n(B," accept two formats as input:"),B.forEach(t),l=h(f),c=a(f,"UL",{});var oe=i(c);z=a(oe,"LI",{});var Re=i(z);ve=n(Re,"having all inputs as keyword arguments (like PyTorch models), or"),Re.forEach(t),me=h(oe),S=a(oe,"LI",{});var Se=i(S);re=n(Se,"having all inputs as a list, tuple or dict in the first positional argument."),Se.forEach(t),oe.forEach(t),Z=h(f),T=a(f,"P",{});var x=i(T);Te=n(x,`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 `),K=a(x,"CODE",{});var Ue=i(K);ye=n(Ue,"model.fit()"),Ue.forEach(t),ue=n(x,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=a(x,"CODE",{});var Ke=i(L);we=n(Ke,"model.fit()"),Ke.forEach(t),ie=n(x,` supports! If, however, you want to use the second
format outside of Keras methods like `),Q=a(x,"CODE",{});var Ie=i(Q);$e=n(Ie,"fit()"),Ie.forEach(t),fe=n(x," and "),R=a(x,"CODE",{});var Ae=i(R);Me=n(Ae,"predict()"),Ae.forEach(t),ge=n(x,`, such as when creating your own layers or models with
the Keras `),U=a(x,"CODE",{});var He=i(U);ce=n(He,"Functional"),He.forEach(t),Fe=n(x,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),x.forEach(t),te=h(f),P=a(f,"UL",{});var D=i(P);N=a(D,"LI",{});var H=i(N);_e=n(H,"a single Tensor with "),W=a(H,"CODE",{});var Oe=i(W);pe=n(Oe,"input_ids"),Oe.forEach(t),Ee=n(H," only and nothing else: "),V=a(H,"CODE",{});var Qe=i(V);he=n(Qe,"model(input_ids)"),Qe.forEach(t),H.forEach(t),Be=h(D),C=a(D,"LI",{});var Y=i(C);se=n(Y,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),G=a(Y,"CODE",{});var Le=i(G);le=n(Le,"model([input_ids, attention_mask])"),Le.forEach(t),xe=n(Y," or "),J=a(Y,"CODE",{});var De=i(J);de=n(De,"model([input_ids, attention_mask, token_type_ids])"),De.forEach(t),Y.forEach(t),ze=h(D),O=a(D,"LI",{});var je=i(O);be=n(je,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),I=a(je,"CODE",{});var We=i(I);ae=n(We,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),We.forEach(t),je.forEach(t),D.forEach(t),ee=h(f),j=a(f,"P",{});var X=i(j);qe=n(X,`Note that when creating models and layers with
`),A=a(X,"A",{href:!0,rel:!0});var Ce=i(A);Pe=n(Ce,"subclassing"),Ce.forEach(t),ke=n(X,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),X.forEach(t),this.h()},h(){g(A,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),g(A,"rel","nofollow")},m(f,B){_(f,d,B),e(d,v),e(d,m),e(m,u),e(d,k),_(f,l,B),_(f,c,B),e(c,z),e(z,ve),e(c,me),e(c,S),e(S,re),_(f,Z,B),_(f,T,B),e(T,Te),e(T,K),e(K,ye),e(T,ue),e(T,L),e(L,we),e(T,ie),e(T,Q),e(Q,$e),e(T,fe),e(T,R),e(R,Me),e(T,ge),e(T,U),e(U,ce),e(T,Fe),_(f,te,B),_(f,P,B),e(P,N),e(N,_e),e(N,W),e(W,pe),e(N,Ee),e(N,V),e(V,he),e(P,Be),e(P,C),e(C,se),e(C,G),e(G,le),e(C,xe),e(C,J),e(J,de),e(P,ze),e(P,O),e(O,be),e(O,I),e(I,ae),_(f,ee,B),_(f,j,B),e(j,qe),e(j,A),e(A,Pe),e(j,ke)},d(f){f&&t(d),f&&t(l),f&&t(c),f&&t(Z),f&&t(T),f&&t(te),f&&t(P),f&&t(ee),f&&t(j)}}}function o1(q){let d,v,m,u,k;return{c(){d=r("p"),v=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),u=o("Module"),k=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function n1(q){let d,v,m,u,k;return u=new Ye({props:{code:`import tensorflow as tf
from transformers import AutoTokenizer, TFMobileBertForPreTraining

tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
model = TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased")
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
outputs = model(input_ids)
prediction_scores, seq_relationship_scores = outputs[:2]`,highlighted:`<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><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFMobileBertForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/mobilebert-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMobileBertForPreTraining.from_pretrained(<span class="hljs-string">&quot;google/mobilebert-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tf.constant(tokenizer.encode(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>))[<span class="hljs-literal">None</span>, :]  <span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>prediction_scores, seq_relationship_scores = outputs[:<span class="hljs-number">2</span>]`}}),{c(){d=r("p"),v=o("Examples:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Examples:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function s1(q){let d,v,m,u,k,l,c,z,ve,me,S,re,Z,T,Te,K,ye,ue,L,we,ie,Q,$e,fe,R,Me,ge,U,ce,Fe,te,P,N,_e,W,pe,Ee,V,he,Be,C,se,G,le,xe,J,de,ze,O,be,I,ae,ee,j,qe,A,Pe,ke;return{c(){d=r("p"),v=o("TensorFlow models and layers in "),m=r("code"),u=o("transformers"),k=o(" accept two formats as input:"),l=p(),c=r("ul"),z=r("li"),ve=o("having all inputs as keyword arguments (like PyTorch models), or"),me=p(),S=r("li"),re=o("having all inputs as a list, tuple or dict in the first positional argument."),Z=p(),T=r("p"),Te=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),K=r("code"),ye=o("model.fit()"),ue=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=r("code"),we=o("model.fit()"),ie=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),Q=r("code"),$e=o("fit()"),fe=o(" and "),R=r("code"),Me=o("predict()"),ge=o(`, such as when creating your own layers or models with
the Keras `),U=r("code"),ce=o("Functional"),Fe=o(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),te=p(),P=r("ul"),N=r("li"),_e=o("a single Tensor with "),W=r("code"),pe=o("input_ids"),Ee=o(" only and nothing else: "),V=r("code"),he=o("model(input_ids)"),Be=p(),C=r("li"),se=o(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),G=r("code"),le=o("model([input_ids, attention_mask])"),xe=o(" or "),J=r("code"),de=o("model([input_ids, attention_mask, token_type_ids])"),ze=p(),O=r("li"),be=o(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),I=r("code"),ae=o('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),ee=p(),j=r("p"),qe=o(`Note that when creating models and layers with
`),A=r("a"),Pe=o("subclassing"),ke=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=a(f,"P",{});var B=i(d);v=n(B,"TensorFlow models and layers in "),m=a(B,"CODE",{});var Ne=i(m);u=n(Ne,"transformers"),Ne.forEach(t),k=n(B," accept two formats as input:"),B.forEach(t),l=h(f),c=a(f,"UL",{});var oe=i(c);z=a(oe,"LI",{});var Re=i(z);ve=n(Re,"having all inputs as keyword arguments (like PyTorch models), or"),Re.forEach(t),me=h(oe),S=a(oe,"LI",{});var Se=i(S);re=n(Se,"having all inputs as a list, tuple or dict in the first positional argument."),Se.forEach(t),oe.forEach(t),Z=h(f),T=a(f,"P",{});var x=i(T);Te=n(x,`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 `),K=a(x,"CODE",{});var Ue=i(K);ye=n(Ue,"model.fit()"),Ue.forEach(t),ue=n(x,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=a(x,"CODE",{});var Ke=i(L);we=n(Ke,"model.fit()"),Ke.forEach(t),ie=n(x,` supports! If, however, you want to use the second
format outside of Keras methods like `),Q=a(x,"CODE",{});var Ie=i(Q);$e=n(Ie,"fit()"),Ie.forEach(t),fe=n(x," and "),R=a(x,"CODE",{});var Ae=i(R);Me=n(Ae,"predict()"),Ae.forEach(t),ge=n(x,`, such as when creating your own layers or models with
the Keras `),U=a(x,"CODE",{});var He=i(U);ce=n(He,"Functional"),He.forEach(t),Fe=n(x,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),x.forEach(t),te=h(f),P=a(f,"UL",{});var D=i(P);N=a(D,"LI",{});var H=i(N);_e=n(H,"a single Tensor with "),W=a(H,"CODE",{});var Oe=i(W);pe=n(Oe,"input_ids"),Oe.forEach(t),Ee=n(H," only and nothing else: "),V=a(H,"CODE",{});var Qe=i(V);he=n(Qe,"model(input_ids)"),Qe.forEach(t),H.forEach(t),Be=h(D),C=a(D,"LI",{});var Y=i(C);se=n(Y,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),G=a(Y,"CODE",{});var Le=i(G);le=n(Le,"model([input_ids, attention_mask])"),Le.forEach(t),xe=n(Y," or "),J=a(Y,"CODE",{});var De=i(J);de=n(De,"model([input_ids, attention_mask, token_type_ids])"),De.forEach(t),Y.forEach(t),ze=h(D),O=a(D,"LI",{});var je=i(O);be=n(je,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),I=a(je,"CODE",{});var We=i(I);ae=n(We,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),We.forEach(t),je.forEach(t),D.forEach(t),ee=h(f),j=a(f,"P",{});var X=i(j);qe=n(X,`Note that when creating models and layers with
`),A=a(X,"A",{href:!0,rel:!0});var Ce=i(A);Pe=n(Ce,"subclassing"),Ce.forEach(t),ke=n(X,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),X.forEach(t),this.h()},h(){g(A,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),g(A,"rel","nofollow")},m(f,B){_(f,d,B),e(d,v),e(d,m),e(m,u),e(d,k),_(f,l,B),_(f,c,B),e(c,z),e(z,ve),e(c,me),e(c,S),e(S,re),_(f,Z,B),_(f,T,B),e(T,Te),e(T,K),e(K,ye),e(T,ue),e(T,L),e(L,we),e(T,ie),e(T,Q),e(Q,$e),e(T,fe),e(T,R),e(R,Me),e(T,ge),e(T,U),e(U,ce),e(T,Fe),_(f,te,B),_(f,P,B),e(P,N),e(N,_e),e(N,W),e(W,pe),e(N,Ee),e(N,V),e(V,he),e(P,Be),e(P,C),e(C,se),e(C,G),e(G,le),e(C,xe),e(C,J),e(J,de),e(P,ze),e(P,O),e(O,be),e(O,I),e(I,ae),_(f,ee,B),_(f,j,B),e(j,qe),e(j,A),e(A,Pe),e(j,ke)},d(f){f&&t(d),f&&t(l),f&&t(c),f&&t(Z),f&&t(T),f&&t(te),f&&t(P),f&&t(ee),f&&t(j)}}}function r1(q){let d,v,m,u,k;return{c(){d=r("p"),v=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),u=o("Module"),k=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function a1(q){let d,v,m,u,k;return u=new Ye({props:{code:`from transformers import AutoTokenizer, TFMobileBertForMaskedLM
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
model = TFMobileBertForMaskedLM.from_pretrained("google/mobilebert-uncased")

inputs = tokenizer("The capital of France is [MASK].", return_tensors="tf")
logits = model(**inputs).logits

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

predicted_token_id = tf.math.argmax(selected_logits, axis=-1)
tokenizer.decode(predicted_token_id)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFMobileBertForMaskedLM
<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/mobilebert-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMobileBertForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google/mobilebert-uncased&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>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(predicted_token_id)
<span class="hljs-string">&#x27;paris&#x27;</span>`}}),{c(){d=r("p"),v=o("Example:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Example:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function i1(q){let d,v;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)
round(float(outputs.loss), 2)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tokenizer(<span class="hljs-string">&quot;The capital of France is Paris.&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># mask labels of non-[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)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(<span class="hljs-built_in">float</span>(outputs.loss), <span class="hljs-number">2</span>)
<span class="hljs-number">0.57</span>`}}),{c(){y(d.$$.fragment)},l(m){w(d.$$.fragment,m)},m(m,u){$(d,m,u),v=!0},p:Je,i(m){v||(M(d.$$.fragment,m),v=!0)},o(m){F(d.$$.fragment,m),v=!1},d(m){E(d,m)}}}function l1(q){let d,v,m,u,k,l,c,z,ve,me,S,re,Z,T,Te,K,ye,ue,L,we,ie,Q,$e,fe,R,Me,ge,U,ce,Fe,te,P,N,_e,W,pe,Ee,V,he,Be,C,se,G,le,xe,J,de,ze,O,be,I,ae,ee,j,qe,A,Pe,ke;return{c(){d=r("p"),v=o("TensorFlow models and layers in "),m=r("code"),u=o("transformers"),k=o(" accept two formats as input:"),l=p(),c=r("ul"),z=r("li"),ve=o("having all inputs as keyword arguments (like PyTorch models), or"),me=p(),S=r("li"),re=o("having all inputs as a list, tuple or dict in the first positional argument."),Z=p(),T=r("p"),Te=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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`),G=a(Y,"CODE",{});var Le=i(G);le=n(Le,"model([input_ids, attention_mask])"),Le.forEach(t),xe=n(Y," or "),J=a(Y,"CODE",{});var De=i(J);de=n(De,"model([input_ids, attention_mask, token_type_ids])"),De.forEach(t),Y.forEach(t),ze=h(D),O=a(D,"LI",{});var je=i(O);be=n(je,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
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`),A=a(X,"A",{href:!0,rel:!0});var Ce=i(A);Pe=n(Ce,"subclassing"),Ce.forEach(t),ke=n(X,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),X.forEach(t),this.h()},h(){g(A,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),g(A,"rel","nofollow")},m(f,B){_(f,d,B),e(d,v),e(d,m),e(m,u),e(d,k),_(f,l,B),_(f,c,B),e(c,z),e(z,ve),e(c,me),e(c,S),e(S,re),_(f,Z,B),_(f,T,B),e(T,Te),e(T,K),e(K,ye),e(T,ue),e(T,L),e(L,we),e(T,ie),e(T,Q),e(Q,$e),e(T,fe),e(T,R),e(R,Me),e(T,ge),e(T,U),e(U,ce),e(T,Fe),_(f,te,B),_(f,P,B),e(P,N),e(N,_e),e(N,W),e(W,pe),e(N,Ee),e(N,V),e(V,he),e(P,Be),e(P,C),e(C,se),e(C,G),e(G,le),e(C,xe),e(C,J),e(J,de),e(P,ze),e(P,O),e(O,be),e(O,I),e(I,ae),_(f,ee,B),_(f,j,B),e(j,qe),e(j,A),e(A,Pe),e(j,ke)},d(f){f&&t(d),f&&t(l),f&&t(c),f&&t(Z),f&&t(T),f&&t(te),f&&t(P),f&&t(ee),f&&t(j)}}}function d1(q){let d,v,m,u,k;return{c(){d=r("p"),v=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),u=o("Module"),k=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function c1(q){let d,v,m,u,k;return u=new Ye({props:{code:`import tensorflow as tf
from transformers import AutoTokenizer, TFMobileBertForNextSentencePrediction

tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
model = TFMobileBertForNextSentencePrediction.from_pretrained("google/mobilebert-uncased")

prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
next_sentence = "The sky is blue due to the shorter wavelength of blue light."
encoding = tokenizer(prompt, next_sentence, return_tensors="tf")

logits = model(encoding["input_ids"], token_type_ids=encoding["token_type_ids"])[0]`,highlighted:`<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><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFMobileBertForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/mobilebert-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMobileBertForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;google/mobilebert-uncased&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>next_sentence = <span class="hljs-string">&quot;The sky is blue due to the shorter wavelength of blue light.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = tokenizer(prompt, next_sentence, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = model(encoding[<span class="hljs-string">&quot;input_ids&quot;</span>], token_type_ids=encoding[<span class="hljs-string">&quot;token_type_ids&quot;</span>])[<span class="hljs-number">0</span>]`}}),{c(){d=r("p"),v=o("Examples:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Examples:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function p1(q){let d,v,m,u,k,l,c,z,ve,me,S,re,Z,T,Te,K,ye,ue,L,we,ie,Q,$e,fe,R,Me,ge,U,ce,Fe,te,P,N,_e,W,pe,Ee,V,he,Be,C,se,G,le,xe,J,de,ze,O,be,I,ae,ee,j,qe,A,Pe,ke;return{c(){d=r("p"),v=o("TensorFlow models and layers in "),m=r("code"),u=o("transformers"),k=o(" accept two formats as input:"),l=p(),c=r("ul"),z=r("li"),ve=o("having all inputs as keyword arguments (like PyTorch models), or"),me=p(),S=r("li"),re=o("having all inputs as a list, tuple or dict in the first positional argument."),Z=p(),T=r("p"),Te=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),K=r("code"),ye=o("model.fit()"),ue=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=r("code"),we=o("model.fit()"),ie=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),Q=r("code"),$e=o("fit()"),fe=o(" and "),R=r("code"),Me=o("predict()"),ge=o(`, such as when creating your own layers or models with
the Keras `),U=r("code"),ce=o("Functional"),Fe=o(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),te=p(),P=r("ul"),N=r("li"),_e=o("a single Tensor with "),W=r("code"),pe=o("input_ids"),Ee=o(" only and nothing else: "),V=r("code"),he=o("model(input_ids)"),Be=p(),C=r("li"),se=o(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
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`),I=r("code"),ae=o('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),ee=p(),j=r("p"),qe=o(`Note that when creating models and layers with
`),A=r("a"),Pe=o("subclassing"),ke=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=a(f,"P",{});var B=i(d);v=n(B,"TensorFlow models and layers in "),m=a(B,"CODE",{});var Ne=i(m);u=n(Ne,"transformers"),Ne.forEach(t),k=n(B," accept two formats as input:"),B.forEach(t),l=h(f),c=a(f,"UL",{});var oe=i(c);z=a(oe,"LI",{});var Re=i(z);ve=n(Re,"having all inputs as keyword arguments (like PyTorch models), or"),Re.forEach(t),me=h(oe),S=a(oe,"LI",{});var Se=i(S);re=n(Se,"having all inputs as a list, tuple or dict in the first positional argument."),Se.forEach(t),oe.forEach(t),Z=h(f),T=a(f,"P",{});var x=i(T);Te=n(x,`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 `),L=a(x,"CODE",{});var Ke=i(L);we=n(Ke,"model.fit()"),Ke.forEach(t),ie=n(x,` supports! If, however, you want to use the second
format outside of Keras methods like `),Q=a(x,"CODE",{});var Ie=i(Q);$e=n(Ie,"fit()"),Ie.forEach(t),fe=n(x," and "),R=a(x,"CODE",{});var Ae=i(R);Me=n(Ae,"predict()"),Ae.forEach(t),ge=n(x,`, such as when creating your own layers or models with
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positional argument:`),x.forEach(t),te=h(f),P=a(f,"UL",{});var D=i(P);N=a(D,"LI",{});var H=i(N);_e=n(H,"a single Tensor with "),W=a(H,"CODE",{});var Oe=i(W);pe=n(Oe,"input_ids"),Oe.forEach(t),Ee=n(H," only and nothing else: "),V=a(H,"CODE",{});var Qe=i(V);he=n(Qe,"model(input_ids)"),Qe.forEach(t),H.forEach(t),Be=h(D),C=a(D,"LI",{});var Y=i(C);se=n(Y,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),G=a(Y,"CODE",{});var Le=i(G);le=n(Le,"model([input_ids, attention_mask])"),Le.forEach(t),xe=n(Y," or "),J=a(Y,"CODE",{});var De=i(J);de=n(De,"model([input_ids, attention_mask, token_type_ids])"),De.forEach(t),Y.forEach(t),ze=h(D),O=a(D,"LI",{});var je=i(O);be=n(je,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),I=a(je,"CODE",{});var We=i(I);ae=n(We,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),We.forEach(t),je.forEach(t),D.forEach(t),ee=h(f),j=a(f,"P",{});var X=i(j);qe=n(X,`Note that when creating models and layers with
`),A=a(X,"A",{href:!0,rel:!0});var Ce=i(A);Pe=n(Ce,"subclassing"),Ce.forEach(t),ke=n(X,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),X.forEach(t),this.h()},h(){g(A,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),g(A,"rel","nofollow")},m(f,B){_(f,d,B),e(d,v),e(d,m),e(m,u),e(d,k),_(f,l,B),_(f,c,B),e(c,z),e(z,ve),e(c,me),e(c,S),e(S,re),_(f,Z,B),_(f,T,B),e(T,Te),e(T,K),e(K,ye),e(T,ue),e(T,L),e(L,we),e(T,ie),e(T,Q),e(Q,$e),e(T,fe),e(T,R),e(R,Me),e(T,ge),e(T,U),e(U,ce),e(T,Fe),_(f,te,B),_(f,P,B),e(P,N),e(N,_e),e(N,W),e(W,pe),e(N,Ee),e(N,V),e(V,he),e(P,Be),e(P,C),e(C,se),e(C,G),e(G,le),e(C,xe),e(C,J),e(J,de),e(P,ze),e(P,O),e(O,be),e(O,I),e(I,ae),_(f,ee,B),_(f,j,B),e(j,qe),e(j,A),e(A,Pe),e(j,ke)},d(f){f&&t(d),f&&t(l),f&&t(c),f&&t(Z),f&&t(T),f&&t(te),f&&t(P),f&&t(ee),f&&t(j)}}}function h1(q){let d,v,m,u,k;return{c(){d=r("p"),v=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),u=o("Module"),k=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function m1(q){let d,v,m,u,k;return u=new Ye({props:{code:`from transformers import AutoTokenizer, TFMobileBertForSequenceClassification
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("vumichien/emo-mobilebert")
model = TFMobileBertForSequenceClassification.from_pretrained("vumichien/emo-mobilebert")

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])
model.config.id2label[predicted_class_id]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFMobileBertForSequenceClassification
<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;vumichien/emo-mobilebert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMobileBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;vumichien/emo-mobilebert&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>])
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.id2label[predicted_class_id]
<span class="hljs-string">&#x27;others&#x27;</span>`}}),{c(){d=r("p"),v=o("Example:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Example:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function u1(q){let d,v;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 = TFMobileBertForSequenceClassification.from_pretrained("vumichien/emo-mobilebert", num_labels=num_labels)\n\nlabels = tf.constant(1)\nloss = model(**inputs, labels=labels).loss\nround(float(loss), 2)',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>
<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 = TFMobileBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;vumichien/emo-mobilebert&quot;</span>, num_labels=num_labels)

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tf.constant(<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(<span class="hljs-built_in">float</span>(loss), <span class="hljs-number">2</span>)
<span class="hljs-number">4.72</span>`}}),{c(){y(d.$$.fragment)},l(m){w(d.$$.fragment,m)},m(m,u){$(d,m,u),v=!0},p:Je,i(m){v||(M(d.$$.fragment,m),v=!0)},o(m){F(d.$$.fragment,m),v=!1},d(m){E(d,m)}}}function f1(q){let d,v,m,u,k,l,c,z,ve,me,S,re,Z,T,Te,K,ye,ue,L,we,ie,Q,$e,fe,R,Me,ge,U,ce,Fe,te,P,N,_e,W,pe,Ee,V,he,Be,C,se,G,le,xe,J,de,ze,O,be,I,ae,ee,j,qe,A,Pe,ke;return{c(){d=r("p"),v=o("TensorFlow models and layers in "),m=r("code"),u=o("transformers"),k=o(" accept two formats as input:"),l=p(),c=r("ul"),z=r("li"),ve=o("having all inputs as keyword arguments (like PyTorch models), or"),me=p(),S=r("li"),re=o("having all inputs as a list, tuple or dict in the first positional argument."),Z=p(),T=r("p"),Te=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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`),A=a(X,"A",{href:!0,rel:!0});var Ce=i(A);Pe=n(Ce,"subclassing"),Ce.forEach(t),ke=n(X,` 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=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function _1(q){let d,v,m,u,k;return u=new Ye({props:{code:`from transformers import AutoTokenizer, TFMobileBertForMultipleChoice
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
model = TFMobileBertForMultipleChoice.from_pretrained("google/mobilebert-uncased")

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, TFMobileBertForMultipleChoice
<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/mobilebert-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMobileBertForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google/mobilebert-uncased&quot;</span>)

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># the linear classifier still needs to be trained</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=r("p"),v=o("Example:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Example:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function b1(q){let d,v,m,u,k,l,c,z,ve,me,S,re,Z,T,Te,K,ye,ue,L,we,ie,Q,$e,fe,R,Me,ge,U,ce,Fe,te,P,N,_e,W,pe,Ee,V,he,Be,C,se,G,le,xe,J,de,ze,O,be,I,ae,ee,j,qe,A,Pe,ke;return{c(){d=r("p"),v=o("TensorFlow models and layers in "),m=r("code"),u=o("transformers"),k=o(" accept two formats as input:"),l=p(),c=r("ul"),z=r("li"),ve=o("having all inputs as keyword arguments (like PyTorch models), or"),me=p(),S=r("li"),re=o("having all inputs as a list, tuple or dict in the first positional argument."),Z=p(),T=r("p"),Te=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),K=r("code"),ye=o("model.fit()"),ue=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=r("code"),we=o("model.fit()"),ie=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),Q=r("code"),$e=o("fit()"),fe=o(" and "),R=r("code"),Me=o("predict()"),ge=o(`, such as when creating your own layers or models with
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`),A=r("a"),Pe=o("subclassing"),ke=o(` then you don\u2019t need to worry
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positional argument:`),x.forEach(t),te=h(f),P=a(f,"UL",{});var D=i(P);N=a(D,"LI",{});var H=i(N);_e=n(H,"a single Tensor with "),W=a(H,"CODE",{});var Oe=i(W);pe=n(Oe,"input_ids"),Oe.forEach(t),Ee=n(H," only and nothing else: "),V=a(H,"CODE",{});var Qe=i(V);he=n(Qe,"model(input_ids)"),Qe.forEach(t),H.forEach(t),Be=h(D),C=a(D,"LI",{});var Y=i(C);se=n(Y,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
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`),A=a(X,"A",{href:!0,rel:!0});var Ce=i(A);Pe=n(Ce,"subclassing"),Ce.forEach(t),ke=n(X,` 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!`),X.forEach(t),this.h()},h(){g(A,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),g(A,"rel","nofollow")},m(f,B){_(f,d,B),e(d,v),e(d,m),e(m,u),e(d,k),_(f,l,B),_(f,c,B),e(c,z),e(z,ve),e(c,me),e(c,S),e(S,re),_(f,Z,B),_(f,T,B),e(T,Te),e(T,K),e(K,ye),e(T,ue),e(T,L),e(L,we),e(T,ie),e(T,Q),e(Q,$e),e(T,fe),e(T,R),e(R,Me),e(T,ge),e(T,U),e(U,ce),e(T,Fe),_(f,te,B),_(f,P,B),e(P,N),e(N,_e),e(N,W),e(W,pe),e(N,Ee),e(N,V),e(V,he),e(P,Be),e(P,C),e(C,se),e(C,G),e(G,le),e(C,xe),e(C,J),e(J,de),e(P,ze),e(P,O),e(O,be),e(O,I),e(I,ae),_(f,ee,B),_(f,j,B),e(j,qe),e(j,A),e(A,Pe),e(j,ke)},d(f){f&&t(d),f&&t(l),f&&t(c),f&&t(Z),f&&t(T),f&&t(te),f&&t(P),f&&t(ee),f&&t(j)}}}function k1(q){let d,v,m,u,k;return{c(){d=r("p"),v=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),u=o("Module"),k=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(c,"CODE",{});var z=i(m);u=n(z,"Module"),z.forEach(t),k=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,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function v1(q){let d,v,m,u,k;return u=new Ye({props:{code:`from transformers import AutoTokenizer, TFMobileBertForTokenClassification
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("vumichien/mobilebert-finetuned-ner")
model = TFMobileBertForTokenClassification.from_pretrained("vumichien/mobilebert-finetuned-ner")

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()]
predicted_tokens_classes`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFMobileBertForTokenClassification
<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;vumichien/mobilebert-finetuned-ner&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMobileBertForTokenClassification.from_pretrained(<span class="hljs-string">&quot;vumichien/mobilebert-finetuned-ner&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()]
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_tokens_classes
[<span class="hljs-string">&#x27;I-ORG&#x27;</span>, <span class="hljs-string">&#x27;I-ORG&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>, <span class="hljs-string">&#x27;I-LOC&#x27;</span>, <span class="hljs-string">&#x27;O&#x27;</span>, <span class="hljs-string">&#x27;I-LOC&#x27;</span>, <span class="hljs-string">&#x27;I-LOC&#x27;</span>]`}}),{c(){d=r("p"),v=o("Example:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Example:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function T1(q){let d,v;return d=new Ye({props:{code:`labels = predicted_token_class_ids
loss = tf.math.reduce_mean(model(**inputs, labels=labels).loss)
round(float(loss), 2)`,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)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(<span class="hljs-built_in">float</span>(loss), <span class="hljs-number">2</span>)
<span class="hljs-number">0.03</span>`}}),{c(){y(d.$$.fragment)},l(m){w(d.$$.fragment,m)},m(m,u){$(d,m,u),v=!0},p:Je,i(m){v||(M(d.$$.fragment,m),v=!0)},o(m){F(d.$$.fragment,m),v=!1},d(m){E(d,m)}}}function y1(q){let d,v,m,u,k,l,c,z,ve,me,S,re,Z,T,Te,K,ye,ue,L,we,ie,Q,$e,fe,R,Me,ge,U,ce,Fe,te,P,N,_e,W,pe,Ee,V,he,Be,C,se,G,le,xe,J,de,ze,O,be,I,ae,ee,j,qe,A,Pe,ke;return{c(){d=r("p"),v=o("TensorFlow models and layers in "),m=r("code"),u=o("transformers"),k=o(" accept two formats as input:"),l=p(),c=r("ul"),z=r("li"),ve=o("having all inputs as keyword arguments (like PyTorch models), or"),me=p(),S=r("li"),re=o("having all inputs as a list, tuple or dict in the first positional argument."),Z=p(),T=r("p"),Te=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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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.`),c.forEach(t)},m(l,c){_(l,d,c),e(d,v),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function $1(q){let d,v,m,u,k;return u=new Ye({props:{code:`from transformers import AutoTokenizer, TFMobileBertForQuestionAnswering
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("vumichien/mobilebert-uncased-squad-v2")
model = TFMobileBertForQuestionAnswering.from_pretrained("vumichien/mobilebert-uncased-squad-v2")

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]
tokenizer.decode(predict_answer_tokens)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFMobileBertForQuestionAnswering
<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;vumichien/mobilebert-uncased-squad-v2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMobileBertForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;vumichien/mobilebert-uncased-squad-v2&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>]
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(predict_answer_tokens)
<span class="hljs-string">&#x27;a nice puppet&#x27;</span>`}}),{c(){d=r("p"),v=o("Example:"),m=p(),y(u.$$.fragment)},l(l){d=a(l,"P",{});var c=i(d);v=n(c,"Example:"),c.forEach(t),m=h(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,v),_(l,m,c),$(u,l,c),k=!0},p:Je,i(l){k||(M(u.$$.fragment,l),k=!0)},o(l){F(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function M1(q){let d,v;return d=new Ye({props:{code:`# target is "nice puppet"
target_start_index = tf.constant([12])
target_end_index = tf.constant([13])

outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
loss = tf.math.reduce_mean(outputs.loss)
round(float(loss), 2)`,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">12</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>target_end_index = tf.constant([<span class="hljs-number">13</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)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(<span class="hljs-built_in">float</span>(loss), <span class="hljs-number">2</span>)
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Vocabulary size of the MobileBERT 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/mobilebert#transformers.MobileBertModel">MobileBertModel</a> or <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.TFMobileBertModel">TFMobileBertModel</a>.`,name:"vocab_size"},{anchor:"transformers.MobileBertConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.MobileBertConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 24) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.MobileBertConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.MobileBertConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.MobileBertConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;relu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.MobileBertConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.MobileBertConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.MobileBertConfig.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.MobileBertConfig.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/mobilebert#transformers.MobileBertModel">MobileBertModel</a> or
<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.TFMobileBertModel">TFMobileBertModel</a>.`,name:"type_vocab_size"},{anchor:"transformers.MobileBertConfig.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.MobileBertConfig.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.MobileBertConfig.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
The ID of the token in the word embedding to use as padding.`,name:"pad_token_id"},{anchor:"transformers.MobileBertConfig.embedding_size",description:`<strong>embedding_size</strong> (<code>int</code>, <em>optional</em>, defaults to 128) &#x2014;
The dimension of the word embedding vectors.`,name:"embedding_size"},{anchor:"transformers.MobileBertConfig.trigram_input",description:`<strong>trigram_input</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Use a convolution of trigram as input.`,name:"trigram_input"},{anchor:"transformers.MobileBertConfig.use_bottleneck",description:`<strong>use_bottleneck</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to use bottleneck in BERT.`,name:"use_bottleneck"},{anchor:"transformers.MobileBertConfig.intra_bottleneck_size",description:`<strong>intra_bottleneck_size</strong> (<code>int</code>, <em>optional</em>, defaults to 128) &#x2014;
Size of bottleneck layer output.`,name:"intra_bottleneck_size"},{anchor:"transformers.MobileBertConfig.use_bottleneck_attention",description:`<strong>use_bottleneck_attention</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to use attention inputs from the bottleneck transformation.`,name:"use_bottleneck_attention"},{anchor:"transformers.MobileBertConfig.key_query_shared_bottleneck",description:`<strong>key_query_shared_bottleneck</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to use the same linear transformation for query&amp;key in the bottleneck.`,name:"key_query_shared_bottleneck"},{anchor:"transformers.MobileBertConfig.num_feedforward_networks",description:`<strong>num_feedforward_networks</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
Number of FFNs in a block.`,name:"num_feedforward_networks"},{anchor:"transformers.MobileBertConfig.normalization_type",description:`<strong>normalization_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;no_norm&quot;</code>) &#x2014;
The normalization type in MobileBERT.`,name:"normalization_type"},{anchor:"transformers.MobileBertConfig.classifier_dropout",description:`<strong>classifier_dropout</strong> (<code>float</code>, <em>optional</em>) &#x2014;
The dropout ratio for the classification head.`,name:"classifier_dropout"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/configuration_mobilebert.py#L31"}}),sn=new Ge({props:{anchor:"transformers.MobileBertConfig.example",$$slots:{default:[P2]},$$scope:{ctx:q}}}),As=new Xe({}),Os=new ne({props:{name:"class transformers.MobileBertTokenizer",anchor:"transformers.MobileBertTokenizer",parameters:[{name:"vocab_file",val:""},{name:"do_lower_case",val:" = True"},{name:"do_basic_tokenize",val:" = True"},{name:"never_split",val:" = None"},{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:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MobileBertTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
File containing the vocabulary.`,name:"vocab_file"},{anchor:"transformers.MobileBertTokenizer.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.MobileBertTokenizer.do_basic_tokenize",description:`<strong>do_basic_tokenize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to do basic tokenization before WordPiece.`,name:"do_basic_tokenize"},{anchor:"transformers.MobileBertTokenizer.never_split",description:`<strong>never_split</strong> (<code>Iterable</code>, <em>optional</em>) &#x2014;
Collection of tokens which will never be split during tokenization. Only has an effect when
<code>do_basic_tokenize=True</code>`,name:"never_split"},{anchor:"transformers.MobileBertTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[UNK]&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.MobileBertTokenizer.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.MobileBertTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[PAD]&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.MobileBertTokenizer.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.MobileBertTokenizer.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.MobileBertTokenizer.tokenize_chinese_chars",description:`<strong>tokenize_chinese_chars</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to tokenize Chinese characters.</p>
<p>This should likely be deactivated for Japanese (see this
<a href="https://github.com/huggingface/transformers/issues/328" rel="nofollow">issue</a>).`,name:"tokenize_chinese_chars"},{anchor:"transformers.MobileBertTokenizer.strip_accents",description:`<strong>strip_accents</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for <code>lowercase</code> (as in the original MobileBERT).`,name:"strip_accents"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/tokenization_mobilebert.py#L65"}}),Ss=new ne({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.MobileBertTokenizer.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.MobileBertTokenizer.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.MobileBertTokenizer.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/mobilebert/tokenization_mobilebert.py#L195",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>
`}}),Ls=new ne({props:{name:"convert_tokens_to_string",anchor:"transformers.MobileBertTokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/tokenization_mobilebert.py#L190"}}),Ws=new ne({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.MobileBertTokenizer.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.MobileBertTokenizer.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.MobileBertTokenizer.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/mobilebert/tokenization_mobilebert.py#L248",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>
`}}),ln=new Ge({props:{anchor:"transformers.MobileBertTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[j2]},$$scope:{ctx:q}}}),Rs=new ne({props:{name:"get_special_tokens_mask",anchor:"transformers.MobileBertTokenizer.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.MobileBertTokenizer.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.MobileBertTokenizer.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.MobileBertTokenizer.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/mobilebert/tokenization_mobilebert.py#L220",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>
`}}),Ks=new Xe({}),Hs=new ne({props:{name:"class transformers.MobileBertTokenizerFast",anchor:"transformers.MobileBertTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"do_lower_case",val:" = True"},{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:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MobileBertTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
File containing the vocabulary.`,name:"vocab_file"},{anchor:"transformers.MobileBertTokenizerFast.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.MobileBertTokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[UNK]&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.MobileBertTokenizerFast.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.MobileBertTokenizerFast.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[PAD]&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.MobileBertTokenizerFast.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.MobileBertTokenizerFast.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.MobileBertTokenizerFast.clean_text",description:`<strong>clean_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to clean the text before tokenization by removing any control characters and replacing all
whitespaces by the classic one.`,name:"clean_text"},{anchor:"transformers.MobileBertTokenizerFast.tokenize_chinese_chars",description:`<strong>tokenize_chinese_chars</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see <a href="https://github.com/huggingface/transformers/issues/328" rel="nofollow">this
issue</a>).`,name:"tokenize_chinese_chars"},{anchor:"transformers.MobileBertTokenizerFast.strip_accents",description:`<strong>strip_accents</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for <code>lowercase</code> (as in the original MobileBERT).`,name:"strip_accents"},{anchor:"transformers.MobileBertTokenizerFast.wordpieces_prefix",description:`<strong>wordpieces_prefix</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;##&quot;</code>) &#x2014;
The prefix for subwords.`,name:"wordpieces_prefix"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/tokenization_mobilebert_fast.py#L46"}}),Gs=new ne({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.MobileBertTokenizerFast.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:""},{name:"token_ids_1",val:" = None"}],parametersDescription:[{anchor:"transformers.MobileBertTokenizerFast.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.MobileBertTokenizerFast.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/mobilebert/tokenization_mobilebert_fast.py#L134",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>
`}}),Ys=new ne({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.MobileBertTokenizerFast.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.MobileBertTokenizerFast.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.MobileBertTokenizerFast.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/mobilebert/tokenization_mobilebert_fast.py#L158",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>
`}}),pn=new Ge({props:{anchor:"transformers.MobileBertTokenizerFast.create_token_type_ids_from_sequences.example",$$slots:{default:[C2]},$$scope:{ctx:q}}}),Xs=new Xe({}),Zs=new ne({props:{name:"class transformers.models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput",anchor:"transformers.models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput",parameters:[{name:"loss",val:": typing.Optional[torch.FloatTensor] = None"},{name:"prediction_logits",val:": FloatTensor = None"},{name:"seq_relationship_logits",val:": FloatTensor = None"},{name:"hidden_states",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"attentions",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}],parametersDescription:[{anchor:"transformers.models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput.loss",description:`<strong>loss</strong> (<em>optional</em>, returned when <code>labels</code> is provided, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) &#x2014;
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.`,name:"loss"},{anchor:"transformers.models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput.prediction_logits",description:`<strong>prediction_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) &#x2014;
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).`,name:"prediction_logits"},{anchor:"transformers.models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput.seq_relationship_logits",description:`<strong>seq_relationship_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2)</code>) &#x2014;
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).`,name:"seq_relationship_logits"},{anchor:"transformers.models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput.hidden_states",description:`<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>) &#x2014;
Tuple of <code>torch.FloatTensor</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.`,name:"hidden_states"},{anchor:"transformers.models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput.attentions",description:`<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>) &#x2014;
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.`,name:"attentions"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/modeling_mobilebert.py#L709"}}),tr=new ne({props:{name:"class transformers.models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput",anchor:"transformers.models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput",parameters:[{name:"loss",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"prediction_logits",val:": Tensor = None"},{name:"seq_relationship_logits",val:": Tensor = None"},{name:"hidden_states",val:": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"},{name:"attentions",val:": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}],parametersDescription:[{anchor:"transformers.models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput.prediction_logits",description:`<strong>prediction_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) &#x2014;
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).`,name:"prediction_logits"},{anchor:"transformers.models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput.seq_relationship_logits",description:`<strong>seq_relationship_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, 2)</code>) &#x2014;
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).`,name:"seq_relationship_logits"},{anchor:"transformers.models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput.hidden_states",description:`<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>) &#x2014;
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.`,name:"hidden_states"},{anchor:"transformers.models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput.attentions",description:`<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>) &#x2014;
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.`,name:"attentions"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/modeling_tf_mobilebert.py#L835"}}),nr=new Xe({}),sr=new ne({props:{name:"class transformers.MobileBertModel",anchor:"transformers.MobileBertModel",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = True"}],parametersDescription:[{anchor:"transformers.MobileBertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_mobilebert.py#L812"}}),dr=new ne({props:{name:"forward",anchor:"transformers.MobileBertModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MobileBertModel.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/bert#transformers.BertTokenizer">BertTokenizer</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.MobileBertModel.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.MobileBertModel.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.MobileBertModel.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.MobileBertModel.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.MobileBertModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.MobileBertModel.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.MobileBertModel.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.MobileBertModel.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"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/modeling_mobilebert.py#L842",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),un=new Ve({props:{$$slots:{default:[N2]},$$scope:{ctx:q}}}),fn=new Ge({props:{anchor:"transformers.MobileBertModel.forward.example",$$slots:{default:[A2]},$$scope:{ctx:q}}}),cr=new Xe({}),pr=new ne({props:{name:"class transformers.MobileBertForPreTraining",anchor:"transformers.MobileBertForPreTraining",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.MobileBertForPreTraining.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_mobilebert.py#L925"}}),fr=new ne({props:{name:"forward",anchor:"transformers.MobileBertForPreTraining.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"next_sentence_label",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"return_dict",val:": typing.Optional[torch.FloatTensor] = None"}],parametersDescription:[{anchor:"transformers.MobileBertForPreTraining.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/bert#transformers.BertTokenizer">BertTokenizer</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.MobileBertForPreTraining.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.MobileBertForPreTraining.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.MobileBertForPreTraining.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.MobileBertForPreTraining.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.MobileBertForPreTraining.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.MobileBertForPreTraining.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.MobileBertForPreTraining.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.MobileBertForPreTraining.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.MobileBertForPreTraining.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are ignored (masked), the
loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"},{anchor:"transformers.MobileBertForPreTraining.forward.next_sentence_label",description:`<strong>next_sentence_label</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see <code>input_ids</code> docstring) Indices should be in <code>[0, 1]</code>:</p>
<ul>
<li>0 indicates sequence B is a continuation of sequence A,</li>
<li>1 indicates sequence B is a random sequence.</li>
</ul>`,name:"next_sentence_label"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/modeling_mobilebert.py#L954",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput"
>transformers.models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput</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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<em>optional</em>, returned when <code>labels</code> is provided, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) \u2014 Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.</p>
</li>
<li>
<p><strong>prediction_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>seq_relationship_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2)</code>) \u2014 Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
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 + 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(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/model_doc/mobilebert#transformers.models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput"
>transformers.models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),_n=new Ve({props:{$$slots:{default:[O2]},$$scope:{ctx:q}}}),bn=new Ge({props:{anchor:"transformers.MobileBertForPreTraining.forward.example",$$slots:{default:[D2]},$$scope:{ctx:q}}}),gr=new Xe({}),_r=new ne({props:{name:"class transformers.MobileBertForMaskedLM",anchor:"transformers.MobileBertForMaskedLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.MobileBertForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_mobilebert.py#L1037"}}),yr=new ne({props:{name:"forward",anchor:"transformers.MobileBertForMaskedLM.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MobileBertForMaskedLM.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/bert#transformers.BertTokenizer">BertTokenizer</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.MobileBertForMaskedLM.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.MobileBertForMaskedLM.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.MobileBertForMaskedLM.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.MobileBertForMaskedLM.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.MobileBertForMaskedLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.MobileBertForMaskedLM.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.MobileBertForMaskedLM.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.MobileBertForMaskedLM.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.MobileBertForMaskedLM.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/mobilebert/modeling_mobilebert.py#L1067",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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</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>
`}}),vn=new Ve({props:{$$slots:{default:[S2]},$$scope:{ctx:q}}}),Tn=new Ge({props:{anchor:"transformers.MobileBertForMaskedLM.forward.example",$$slots:{default:[I2]},$$scope:{ctx:q}}}),wr=new Xe({}),$r=new ne({props:{name:"class transformers.MobileBertForNextSentencePrediction",anchor:"transformers.MobileBertForNextSentencePrediction",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.MobileBertForNextSentencePrediction.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_mobilebert.py#L1142"}}),xr=new ne({props:{name:"forward",anchor:"transformers.MobileBertForNextSentencePrediction.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MobileBertForNextSentencePrediction.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/bert#transformers.BertTokenizer">BertTokenizer</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.MobileBertForNextSentencePrediction.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.MobileBertForNextSentencePrediction.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.MobileBertForNextSentencePrediction.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.MobileBertForNextSentencePrediction.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.MobileBertForNextSentencePrediction.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.MobileBertForNextSentencePrediction.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.MobileBertForNextSentencePrediction.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.MobileBertForNextSentencePrediction.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.MobileBertForNextSentencePrediction.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see <code>input_ids</code> docstring) Indices should be in <code>[0, 1]</code>.</p>
<ul>
<li>0 indicates sequence B is a continuation of sequence A,</li>
<li>1 indicates sequence B is a random sequence.</li>
</ul>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/modeling_mobilebert.py#L1152",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.NextSentencePredictorOutput"
>transformers.modeling_outputs.NextSentencePredictorOutput</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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</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>next_sentence_label</code> is provided) \u2014 Next sequence prediction (classification) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2)</code>) \u2014 Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
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.NextSentencePredictorOutput"
>transformers.modeling_outputs.NextSentencePredictorOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),wn=new Ve({props:{$$slots:{default:[L2]},$$scope:{ctx:q}}}),$n=new Ge({props:{anchor:"transformers.MobileBertForNextSentencePrediction.forward.example",$$slots:{default:[W2]},$$scope:{ctx:q}}}),zr=new Xe({}),qr=new ne({props:{name:"class transformers.MobileBertForSequenceClassification",anchor:"transformers.MobileBertForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.MobileBertForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_mobilebert.py#L1246"}}),Nr=new ne({props:{name:"forward",anchor:"transformers.MobileBertForSequenceClassification.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = 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.MobileBertForSequenceClassification.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/bert#transformers.BertTokenizer">BertTokenizer</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.MobileBertForSequenceClassification.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.MobileBertForSequenceClassification.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.MobileBertForSequenceClassification.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.MobileBertForSequenceClassification.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.MobileBertForSequenceClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.MobileBertForSequenceClassification.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.MobileBertForSequenceClassification.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.MobileBertForSequenceClassification.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.MobileBertForSequenceClassification.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/mobilebert/modeling_mobilebert.py#L1262",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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</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>
`}}),Fn=new Ve({props:{$$slots:{default:[R2]},$$scope:{ctx:q}}}),En=new Ge({props:{anchor:"transformers.MobileBertForSequenceClassification.forward.example",$$slots:{default:[U2]},$$scope:{ctx:q}}}),Bn=new Ge({props:{anchor:"transformers.MobileBertForSequenceClassification.forward.example-2",$$slots:{default:[K2]},$$scope:{ctx:q}}}),Ar=new Xe({}),Or=new ne({props:{name:"class transformers.MobileBertForMultipleChoice",anchor:"transformers.MobileBertForMultipleChoice",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.MobileBertForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_mobilebert.py#L1456"}}),Lr=new ne({props:{name:"forward",anchor:"transformers.MobileBertForMultipleChoice.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = 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.MobileBertForMultipleChoice.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/bert#transformers.BertTokenizer">BertTokenizer</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.MobileBertForMultipleChoice.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.MobileBertForMultipleChoice.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.MobileBertForMultipleChoice.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.MobileBertForMultipleChoice.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.MobileBertForMultipleChoice.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.MobileBertForMultipleChoice.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.MobileBertForMultipleChoice.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.MobileBertForMultipleChoice.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.MobileBertForMultipleChoice.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/mobilebert/modeling_mobilebert.py#L1470",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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</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>
`}}),zn=new Ve({props:{$$slots:{default:[H2]},$$scope:{ctx:q}}}),qn=new Ge({props:{anchor:"transformers.MobileBertForMultipleChoice.forward.example",$$slots:{default:[Q2]},$$scope:{ctx:q}}}),Wr=new Xe({}),Rr=new ne({props:{name:"class transformers.MobileBertForTokenClassification",anchor:"transformers.MobileBertForTokenClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.MobileBertForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_mobilebert.py#L1553"}}),Qr=new ne({props:{name:"forward",anchor:"transformers.MobileBertForTokenClassification.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = 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.MobileBertForTokenClassification.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/bert#transformers.BertTokenizer">BertTokenizer</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.MobileBertForTokenClassification.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.MobileBertForTokenClassification.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.MobileBertForTokenClassification.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.MobileBertForTokenClassification.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.MobileBertForTokenClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.MobileBertForTokenClassification.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.MobileBertForTokenClassification.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.MobileBertForTokenClassification.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.MobileBertForTokenClassification.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/mobilebert/modeling_mobilebert.py#L1570",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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</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>
`}}),jn=new Ve({props:{$$slots:{default:[V2]},$$scope:{ctx:q}}}),Cn=new Ge({props:{anchor:"transformers.MobileBertForTokenClassification.forward.example",$$slots:{default:[G2]},$$scope:{ctx:q}}}),Vr=new Xe({}),Gr=new ne({props:{name:"class transformers.MobileBertForQuestionAnswering",anchor:"transformers.MobileBertForQuestionAnswering",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.MobileBertForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_mobilebert.py#L1350"}}),Zr=new ne({props:{name:"forward",anchor:"transformers.MobileBertForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"start_positions",val:": typing.Optional[torch.Tensor] = None"},{name:"end_positions",val:": typing.Optional[torch.Tensor] = 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.MobileBertForQuestionAnswering.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/bert#transformers.BertTokenizer">BertTokenizer</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.MobileBertForQuestionAnswering.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.MobileBertForQuestionAnswering.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.MobileBertForQuestionAnswering.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.MobileBertForQuestionAnswering.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.MobileBertForQuestionAnswering.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.MobileBertForQuestionAnswering.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.MobileBertForQuestionAnswering.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.MobileBertForQuestionAnswering.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.MobileBertForQuestionAnswering.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.MobileBertForQuestionAnswering.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/mobilebert/modeling_mobilebert.py#L1363",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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</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>
`}}),An=new Ve({props:{$$slots:{default:[J2]},$$scope:{ctx:q}}}),On=new Ge({props:{anchor:"transformers.MobileBertForQuestionAnswering.forward.example",$$slots:{default:[Y2]},$$scope:{ctx:q}}}),ea=new Xe({}),ta=new ne({props:{name:"class transformers.TFMobileBertModel",anchor:"transformers.TFMobileBertModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFMobileBertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_tf_mobilebert.py#L967"}}),Sn=new Ve({props:{$$slots:{default:[X2]},$$scope:{ctx:q}}}),ra=new ne({props:{name:"call",anchor:"transformers.TFMobileBertModel.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:"training",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFMobileBertModel.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFMobileBertModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFMobileBertModel.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFMobileBertModel.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFMobileBertModel.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFMobileBertModel.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFMobileBertModel.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.TFMobileBertModel.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.TFMobileBertModel.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.TFMobileBertModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/modeling_tf_mobilebert.py#L972",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</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>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.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</a> or <code>tuple(tf.Tensor)</code></p>
`}}),In=new Ve({props:{$$slots:{default:[Z2]},$$scope:{ctx:q}}}),Ln=new Ge({props:{anchor:"transformers.TFMobileBertModel.call.example",$$slots:{default:[e1]},$$scope:{ctx:q}}}),aa=new Xe({}),ia=new ne({props:{name:"class transformers.TFMobileBertForPreTraining",anchor:"transformers.TFMobileBertForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFMobileBertForPreTraining.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_tf_mobilebert.py#L1026"}}),Rn=new Ve({props:{$$slots:{default:[t1]},$$scope:{ctx:q}}}),pa=new ne({props:{name:"call",anchor:"transformers.TFMobileBertForPreTraining.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:"next_sentence_label",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFMobileBertForPreTraining.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFMobileBertForPreTraining.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFMobileBertForPreTraining.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFMobileBertForPreTraining.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFMobileBertForPreTraining.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFMobileBertForPreTraining.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFMobileBertForPreTraining.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.TFMobileBertForPreTraining.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.TFMobileBertForPreTraining.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.TFMobileBertForPreTraining.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/modeling_tf_mobilebert.py#L1040",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput"
>transformers.models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput</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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>prediction_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>seq_relationship_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, 2)</code>) \u2014 Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
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/model_doc/mobilebert#transformers.models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput"
>transformers.models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Un=new Ve({props:{$$slots:{default:[o1]},$$scope:{ctx:q}}}),Kn=new Ge({props:{anchor:"transformers.TFMobileBertForPreTraining.call.example",$$slots:{default:[n1]},$$scope:{ctx:q}}}),ha=new Xe({}),ma=new ne({props:{name:"class transformers.TFMobileBertForMaskedLM",anchor:"transformers.TFMobileBertForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFMobileBertForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_tf_mobilebert.py#L1121"}}),Qn=new Ve({props:{$$slots:{default:[s1]},$$scope:{ctx:q}}}),ba=new ne({props:{name:"call",anchor:"transformers.TFMobileBertForMaskedLM.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.TFMobileBertForMaskedLM.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFMobileBertForMaskedLM.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFMobileBertForMaskedLM.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFMobileBertForMaskedLM.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFMobileBertForMaskedLM.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFMobileBertForMaskedLM.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFMobileBertForMaskedLM.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.TFMobileBertForMaskedLM.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.TFMobileBertForMaskedLM.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.TFMobileBertForMaskedLM.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFMobileBertForMaskedLM.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are ignored (masked), the
loss is only computed for the tokens with labels`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/modeling_tf_mobilebert.py#L1142",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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</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>
`}}),Vn=new Ve({props:{$$slots:{default:[r1]},$$scope:{ctx:q}}}),Gn=new Ge({props:{anchor:"transformers.TFMobileBertForMaskedLM.call.example",$$slots:{default:[a1]},$$scope:{ctx:q}}}),Jn=new Ge({props:{anchor:"transformers.TFMobileBertForMaskedLM.call.example-2",$$slots:{default:[i1]},$$scope:{ctx:q}}}),ka=new Xe({}),va=new ne({props:{name:"class transformers.TFMobileBertForNextSentencePrediction",anchor:"transformers.TFMobileBertForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFMobileBertForNextSentencePrediction.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_tf_mobilebert.py#L1221"}}),Xn=new Ve({props:{$$slots:{default:[l1]},$$scope:{ctx:q}}}),Ma=new ne({props:{name:"call",anchor:"transformers.TFMobileBertForNextSentencePrediction.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:"next_sentence_label",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFMobileBertForNextSentencePrediction.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFMobileBertForNextSentencePrediction.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFMobileBertForNextSentencePrediction.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFMobileBertForNextSentencePrediction.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFMobileBertForNextSentencePrediction.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFMobileBertForNextSentencePrediction.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFMobileBertForNextSentencePrediction.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.TFMobileBertForNextSentencePrediction.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.TFMobileBertForNextSentencePrediction.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.TFMobileBertForNextSentencePrediction.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/modeling_tf_mobilebert.py#L1231",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFNextSentencePredictorOutput"
>transformers.modeling_tf_outputs.TFNextSentencePredictorOutput</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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</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>next_sentence_label</code> is provided) \u2014 Next sentence prediction loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, 2)</code>) \u2014 Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
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.TFNextSentencePredictorOutput"
>transformers.modeling_tf_outputs.TFNextSentencePredictorOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Zn=new Ve({props:{$$slots:{default:[d1]},$$scope:{ctx:q}}}),es=new Ge({props:{anchor:"transformers.TFMobileBertForNextSentencePrediction.call.example",$$slots:{default:[c1]},$$scope:{ctx:q}}}),Fa=new Xe({}),Ea=new ne({props:{name:"class transformers.TFMobileBertForSequenceClassification",anchor:"transformers.TFMobileBertForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFMobileBertForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_tf_mobilebert.py#L1313"}}),os=new Ve({props:{$$slots:{default:[p1]},$$scope:{ctx:q}}}),qa=new ne({props:{name:"call",anchor:"transformers.TFMobileBertForSequenceClassification.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.TFMobileBertForSequenceClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFMobileBertForSequenceClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFMobileBertForSequenceClassification.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFMobileBertForSequenceClassification.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFMobileBertForSequenceClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFMobileBertForSequenceClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFMobileBertForSequenceClassification.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.TFMobileBertForSequenceClassification.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.TFMobileBertForSequenceClassification.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.TFMobileBertForSequenceClassification.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFMobileBertForSequenceClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/modeling_tf_mobilebert.py#L1336",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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</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>
`}}),ns=new Ve({props:{$$slots:{default:[h1]},$$scope:{ctx:q}}}),ss=new Ge({props:{anchor:"transformers.TFMobileBertForSequenceClassification.call.example",$$slots:{default:[m1]},$$scope:{ctx:q}}}),rs=new Ge({props:{anchor:"transformers.TFMobileBertForSequenceClassification.call.example-2",$$slots:{default:[u1]},$$scope:{ctx:q}}}),Pa=new Xe({}),ja=new ne({props:{name:"class transformers.TFMobileBertForMultipleChoice",anchor:"transformers.TFMobileBertForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFMobileBertForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_tf_mobilebert.py#L1518"}}),is=new Ve({props:{$$slots:{default:[f1]},$$scope:{ctx:q}}}),Oa=new ne({props:{name:"call",anchor:"transformers.TFMobileBertForMultipleChoice.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.TFMobileBertForMultipleChoice.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFMobileBertForMultipleChoice.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFMobileBertForMultipleChoice.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFMobileBertForMultipleChoice.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFMobileBertForMultipleChoice.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFMobileBertForMultipleChoice.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFMobileBertForMultipleChoice.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.TFMobileBertForMultipleChoice.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.TFMobileBertForMultipleChoice.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.TFMobileBertForMultipleChoice.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFMobileBertForMultipleChoice.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the multiple choice classification loss. Indices should be in <code>[0, ..., num_choices]</code>
where <code>num_choices</code> is the size of the second dimension of the input tensors. (See <code>input_ids</code> above)`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/modeling_tf_mobilebert.py#L1547",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput"
>transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</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>
`}}),ls=new Ve({props:{$$slots:{default:[g1]},$$scope:{ctx:q}}}),ds=new Ge({props:{anchor:"transformers.TFMobileBertForMultipleChoice.call.example",$$slots:{default:[_1]},$$scope:{ctx:q}}}),Da=new Xe({}),Sa=new ne({props:{name:"class transformers.TFMobileBertForTokenClassification",anchor:"transformers.TFMobileBertForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFMobileBertForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_tf_mobilebert.py#L1651"}}),ps=new Ve({props:{$$slots:{default:[b1]},$$scope:{ctx:q}}}),Ra=new ne({props:{name:"call",anchor:"transformers.TFMobileBertForTokenClassification.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.TFMobileBertForTokenClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFMobileBertForTokenClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFMobileBertForTokenClassification.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFMobileBertForTokenClassification.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFMobileBertForTokenClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFMobileBertForTokenClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFMobileBertForTokenClassification.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.TFMobileBertForTokenClassification.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.TFMobileBertForTokenClassification.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.TFMobileBertForTokenClassification.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFMobileBertForTokenClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the token classification loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/mobilebert/modeling_tf_mobilebert.py#L1675",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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</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>
`}}),hs=new Ve({props:{$$slots:{default:[k1]},$$scope:{ctx:q}}}),ms=new Ge({props:{anchor:"transformers.TFMobileBertForTokenClassification.call.example",$$slots:{default:[v1]},$$scope:{ctx:q}}}),us=new Ge({props:{anchor:"transformers.TFMobileBertForTokenClassification.call.example-2",$$slots:{default:[T1]},$$scope:{ctx:q}}}),Ua=new Xe({}),Ka=new ne({props:{name:"class transformers.TFMobileBertForQuestionAnswering",anchor:"transformers.TFMobileBertForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFMobileBertForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</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/mobilebert/modeling_tf_mobilebert.py#L1410"}}),gs=new Ve({props:{$$slots:{default:[y1]},$$scope:{ctx:q}}}),Ga=new ne({props:{name:"call",anchor:"transformers.TFMobileBertForQuestionAnswering.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"inputs_embeds",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"start_positions",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"end_positions",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFMobileBertForQuestionAnswering.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFMobileBertForQuestionAnswering.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
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<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFMobileBertForQuestionAnswering.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
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<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFMobileBertForQuestionAnswering.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFMobileBertForQuestionAnswering.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFMobileBertForQuestionAnswering.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFMobileBertForQuestionAnswering.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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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.TFMobileBertForQuestionAnswering.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.TFMobileBertForQuestionAnswering.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFMobileBertForQuestionAnswering.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFMobileBertForQuestionAnswering.call.start_positions",description:`<strong>start_positions</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the start of the labelled span for computing the token classification loss.
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are not taken into account for computing the loss.`,name:"start_positions"},{anchor:"transformers.TFMobileBertForQuestionAnswering.call.end_positions",description:`<strong>end_positions</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
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<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/mobilebert#transformers.MobileBertConfig"
>MobileBertConfig</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"
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for Named-Entity-Recognition (NER) tasks.`),tw.forEach(t),s_=h(ro),Ur=a(ro,"P",{});var lh=i(Ur);r_=n(lh,"This model inherits from "),Fi=a(lh,"A",{href:!0});var ow=i(Fi);a_=n(ow,"PreTrainedModel"),ow.forEach(t),i_=n(lh,`. 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.)`),lh.forEach(t),l_=h(ro),Kr=a(ro,"P",{});var dh=i(Kr);d_=n(dh,"This model is also a PyTorch "),Hr=a(dh,"A",{href:!0,rel:!0});var nw=i(Hr);c_=n(nw,"torch.nn.Module"),nw.forEach(t),p_=n(dh,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),dh.forEach(t),h_=h(ro),jt=a(ro,"DIV",{class:!0});var Es=i(jt);w(Qr.$$.fragment,Es),m_=h(Es),Co=a(Es,"P",{});var nl=i(Co);u_=n(nl,"The "),Ei=a(nl,"A",{href:!0});var sw=i(Ei);f_=n(sw,"MobileBertForTokenClassification"),sw.forEach(t),g_=n(nl," forward method, overrides the "),gd=a(nl,"CODE",{});var rw=i(gd);__=n(rw,"__call__"),rw.forEach(t),b_=n(nl," special method."),nl.forEach(t),k_=h(Es),w(jn.$$.fragment,Es),v_=h(Es),w(Cn.$$.fragment,Es),Es.forEach(t),ro.forEach(t),ip=h(s),No=a(s,"H2",{class:!0});var ch=i(No);Nn=a(ch,"A",{id:!0,class:!0,href:!0});var aw=i(Nn);_d=a(aw,"SPAN",{});var iw=i(_d);w(Vr.$$.fragment,iw),iw.forEach(t),aw.forEach(t),T_=h(ch),bd=a(ch,"SPAN",{});var lw=i(bd);y_=n(lw,"MobileBertForQuestionAnswering"),lw.forEach(t),ch.forEach(t),lp=h(s),_t=a(s,"DIV",{class:!0});var ao=i(_t);w(Gr.$$.fragment,ao),w_=h(ao),Ao=a(ao,"P",{});var sl=i(Ao);$_=n(sl,`MobileBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
linear layers on top of the hidden-states output to compute `),kd=a(sl,"CODE",{});var dw=i(kd);M_=n(dw,"span start logits"),dw.forEach(t),F_=n(sl," and "),vd=a(sl,"CODE",{});var cw=i(vd);E_=n(cw,"span end logits"),cw.forEach(t),B_=n(sl,")."),sl.forEach(t),x_=h(ao),Jr=a(ao,"P",{});var ph=i(Jr);z_=n(ph,"This model inherits from "),Bi=a(ph,"A",{href:!0});var pw=i(Bi);q_=n(pw,"PreTrainedModel"),pw.forEach(t),P_=n(ph,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),ph.forEach(t),j_=h(ao),Yr=a(ao,"P",{});var hh=i(Yr);C_=n(hh,"This model is also a PyTorch "),Xr=a(hh,"A",{href:!0,rel:!0});var hw=i(Xr);N_=n(hw,"torch.nn.Module"),hw.forEach(t),A_=n(hh,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),hh.forEach(t),O_=h(ao),Ct=a(ao,"DIV",{class:!0});var Bs=i(Ct);w(Zr.$$.fragment,Bs),D_=h(Bs),Oo=a(Bs,"P",{});var rl=i(Oo);S_=n(rl,"The "),xi=a(rl,"A",{href:!0});var mw=i(xi);I_=n(mw,"MobileBertForQuestionAnswering"),mw.forEach(t),L_=n(rl," forward method, overrides the "),Td=a(rl,"CODE",{});var uw=i(Td);W_=n(uw,"__call__"),uw.forEach(t),R_=n(rl," special method."),rl.forEach(t),U_=h(Bs),w(An.$$.fragment,Bs),K_=h(Bs),w(On.$$.fragment,Bs),Bs.forEach(t),ao.forEach(t),dp=h(s),Do=a(s,"H2",{class:!0});var mh=i(Do);Dn=a(mh,"A",{id:!0,class:!0,href:!0});var fw=i(Dn);yd=a(fw,"SPAN",{});var gw=i(yd);w(ea.$$.fragment,gw),gw.forEach(t),fw.forEach(t),H_=h(mh),wd=a(mh,"SPAN",{});var _w=i(wd);Q_=n(_w,"TFMobileBertModel"),_w.forEach(t),mh.forEach(t),cp=h(s),tt=a(s,"DIV",{class:!0});var It=i(tt);w(ta.$$.fragment,It),V_=h(It),$d=a(It,"P",{});var bw=i($d);G_=n(bw,"The bare MobileBert Model transformer outputting raw hidden-states without any specific head on top."),bw.forEach(t),J_=h(It),oa=a(It,"P",{});var uh=i(oa);Y_=n(uh,"This model inherits from "),zi=a(uh,"A",{href:!0});var kw=i(zi);X_=n(kw,"TFPreTrainedModel"),kw.forEach(t),Z_=n(uh,`. 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.)`),uh.forEach(t),eb=h(It),na=a(It,"P",{});var fh=i(na);tb=n(fh,"This model is also a "),sa=a(fh,"A",{href:!0,rel:!0});var vw=i(sa);ob=n(vw,"tf.keras.Model"),vw.forEach(t),nb=n(fh,` 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.`),fh.forEach(t),sb=h(It),w(Sn.$$.fragment,It),rb=h(It),Nt=a(It,"DIV",{class:!0});var xs=i(Nt);w(ra.$$.fragment,xs),ab=h(xs),So=a(xs,"P",{});var al=i(So);ib=n(al,"The "),qi=a(al,"A",{href:!0});var Tw=i(qi);lb=n(Tw,"TFMobileBertModel"),Tw.forEach(t),db=n(al," forward method, overrides the "),Md=a(al,"CODE",{});var yw=i(Md);cb=n(yw,"__call__"),yw.forEach(t),pb=n(al," special method."),al.forEach(t),hb=h(xs),w(In.$$.fragment,xs),mb=h(xs),w(Ln.$$.fragment,xs),xs.forEach(t),It.forEach(t),pp=h(s),Io=a(s,"H2",{class:!0});var gh=i(Io);Wn=a(gh,"A",{id:!0,class:!0,href:!0});var ww=i(Wn);Fd=a(ww,"SPAN",{});var $w=i(Fd);w(aa.$$.fragment,$w),$w.forEach(t),ww.forEach(t),ub=h(gh),Ed=a(gh,"SPAN",{});var Mw=i(Ed);fb=n(Mw,"TFMobileBertForPreTraining"),Mw.forEach(t),gh.forEach(t),hp=h(s),ot=a(s,"DIV",{class:!0});var Lt=i(ot);w(ia.$$.fragment,Lt),gb=h(Lt),Lo=a(Lt,"P",{});var il=i(Lo);_b=n(il,"MobileBert Model with two heads on top as done during the pretraining: a "),Bd=a(il,"CODE",{});var Fw=i(Bd);bb=n(Fw,"masked language modeling"),Fw.forEach(t),kb=n(il,` head and a
`),xd=a(il,"CODE",{});var Ew=i(xd);vb=n(Ew,"next sentence prediction (classification)"),Ew.forEach(t),Tb=n(il," head."),il.forEach(t),yb=h(Lt),la=a(Lt,"P",{});var _h=i(la);wb=n(_h,"This model inherits from "),Pi=a(_h,"A",{href:!0});var Bw=i(Pi);$b=n(Bw,"TFPreTrainedModel"),Bw.forEach(t),Mb=n(_h,`. 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.)`),_h.forEach(t),Fb=h(Lt),da=a(Lt,"P",{});var bh=i(da);Eb=n(bh,"This model is also a "),ca=a(bh,"A",{href:!0,rel:!0});var xw=i(ca);Bb=n(xw,"tf.keras.Model"),xw.forEach(t),xb=n(bh,` 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.`),bh.forEach(t),zb=h(Lt),w(Rn.$$.fragment,Lt),qb=h(Lt),At=a(Lt,"DIV",{class:!0});var zs=i(At);w(pa.$$.fragment,zs),Pb=h(zs),Wo=a(zs,"P",{});var ll=i(Wo);jb=n(ll,"The "),ji=a(ll,"A",{href:!0});var zw=i(ji);Cb=n(zw,"TFMobileBertForPreTraining"),zw.forEach(t),Nb=n(ll," forward method, overrides the "),zd=a(ll,"CODE",{});var qw=i(zd);Ab=n(qw,"__call__"),qw.forEach(t),Ob=n(ll," special method."),ll.forEach(t),Db=h(zs),w(Un.$$.fragment,zs),Sb=h(zs),w(Kn.$$.fragment,zs),zs.forEach(t),Lt.forEach(t),mp=h(s),Ro=a(s,"H2",{class:!0});var kh=i(Ro);Hn=a(kh,"A",{id:!0,class:!0,href:!0});var Pw=i(Hn);qd=a(Pw,"SPAN",{});var jw=i(qd);w(ha.$$.fragment,jw),jw.forEach(t),Pw.forEach(t),Ib=h(kh),Pd=a(kh,"SPAN",{});var Cw=i(Pd);Lb=n(Cw,"TFMobileBertForMaskedLM"),Cw.forEach(t),kh.forEach(t),up=h(s),nt=a(s,"DIV",{class:!0});var Wt=i(nt);w(ma.$$.fragment,Wt),Wb=h(Wt),ua=a(Wt,"P",{});var vh=i(ua);Rb=n(vh,"MobileBert Model with a "),jd=a(vh,"CODE",{});var Nw=i(jd);Ub=n(Nw,"language modeling"),Nw.forEach(t),Kb=n(vh," head on top."),vh.forEach(t),Hb=h(Wt),fa=a(Wt,"P",{});var Th=i(fa);Qb=n(Th,"This model inherits from "),Ci=a(Th,"A",{href:!0});var Aw=i(Ci);Vb=n(Aw,"TFPreTrainedModel"),Aw.forEach(t),Gb=n(Th,`. 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.)`),Th.forEach(t),Jb=h(Wt),ga=a(Wt,"P",{});var yh=i(ga);Yb=n(yh,"This model is also a "),_a=a(yh,"A",{href:!0,rel:!0});var Ow=i(_a);Xb=n(Ow,"tf.keras.Model"),Ow.forEach(t),Zb=n(yh,` 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.`),yh.forEach(t),ek=h(Wt),w(Qn.$$.fragment,Wt),tk=h(Wt),kt=a(Wt,"DIV",{class:!0});var io=i(kt);w(ba.$$.fragment,io),ok=h(io),Uo=a(io,"P",{});var dl=i(Uo);nk=n(dl,"The "),Ni=a(dl,"A",{href:!0});var Dw=i(Ni);sk=n(Dw,"TFMobileBertForMaskedLM"),Dw.forEach(t),rk=n(dl," forward method, overrides the "),Cd=a(dl,"CODE",{});var Sw=i(Cd);ak=n(Sw,"__call__"),Sw.forEach(t),ik=n(dl," special method."),dl.forEach(t),lk=h(io),w(Vn.$$.fragment,io),dk=h(io),w(Gn.$$.fragment,io),ck=h(io),w(Jn.$$.fragment,io),io.forEach(t),Wt.forEach(t),fp=h(s),Ko=a(s,"H2",{class:!0});var wh=i(Ko);Yn=a(wh,"A",{id:!0,class:!0,href:!0});var Iw=i(Yn);Nd=a(Iw,"SPAN",{});var Lw=i(Nd);w(ka.$$.fragment,Lw),Lw.forEach(t),Iw.forEach(t),pk=h(wh),Ad=a(wh,"SPAN",{});var Ww=i(Ad);hk=n(Ww,"TFMobileBertForNextSentencePrediction"),Ww.forEach(t),wh.forEach(t),gp=h(s),st=a(s,"DIV",{class:!0});var Rt=i(st);w(va.$$.fragment,Rt),mk=h(Rt),Ta=a(Rt,"P",{});var $h=i(Ta);uk=n($h,"MobileBert Model with a "),Od=a($h,"CODE",{});var Rw=i(Od);fk=n(Rw,"next sentence prediction (classification)"),Rw.forEach(t),gk=n($h," head on top."),$h.forEach(t),_k=h(Rt),ya=a(Rt,"P",{});var Mh=i(ya);bk=n(Mh,"This model inherits from "),Ai=a(Mh,"A",{href:!0});var Uw=i(Ai);kk=n(Uw,"TFPreTrainedModel"),Uw.forEach(t),vk=n(Mh,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Mh.forEach(t),Tk=h(Rt),wa=a(Rt,"P",{});var Fh=i(wa);yk=n(Fh,"This model is also a "),$a=a(Fh,"A",{href:!0,rel:!0});var Kw=i($a);wk=n(Kw,"tf.keras.Model"),Kw.forEach(t),$k=n(Fh,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),Fh.forEach(t),Mk=h(Rt),w(Xn.$$.fragment,Rt),Fk=h(Rt),Ot=a(Rt,"DIV",{class:!0});var qs=i(Ot);w(Ma.$$.fragment,qs),Ek=h(qs),Ho=a(qs,"P",{});var cl=i(Ho);Bk=n(cl,"The "),Oi=a(cl,"A",{href:!0});var Hw=i(Oi);xk=n(Hw,"TFMobileBertForNextSentencePrediction"),Hw.forEach(t),zk=n(cl," forward method, overrides the "),Dd=a(cl,"CODE",{});var Qw=i(Dd);qk=n(Qw,"__call__"),Qw.forEach(t),Pk=n(cl," special method."),cl.forEach(t),jk=h(qs),w(Zn.$$.fragment,qs),Ck=h(qs),w(es.$$.fragment,qs),qs.forEach(t),Rt.forEach(t),_p=h(s),Qo=a(s,"H2",{class:!0});var Eh=i(Qo);ts=a(Eh,"A",{id:!0,class:!0,href:!0});var Vw=i(ts);Sd=a(Vw,"SPAN",{});var Gw=i(Sd);w(Fa.$$.fragment,Gw),Gw.forEach(t),Vw.forEach(t),Nk=h(Eh),Id=a(Eh,"SPAN",{});var Jw=i(Id);Ak=n(Jw,"TFMobileBertForSequenceClassification"),Jw.forEach(t),Eh.forEach(t),bp=h(s),rt=a(s,"DIV",{class:!0});var Ut=i(rt);w(Ea.$$.fragment,Ut),Ok=h(Ut),Ld=a(Ut,"P",{});var Yw=i(Ld);Dk=n(Yw,`MobileBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`),Yw.forEach(t),Sk=h(Ut),Ba=a(Ut,"P",{});var Bh=i(Ba);Ik=n(Bh,"This model inherits from "),Di=a(Bh,"A",{href:!0});var Xw=i(Di);Lk=n(Xw,"TFPreTrainedModel"),Xw.forEach(t),Wk=n(Bh,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Bh.forEach(t),Rk=h(Ut),xa=a(Ut,"P",{});var xh=i(xa);Uk=n(xh,"This model is also a "),za=a(xh,"A",{href:!0,rel:!0});var Zw=i(za);Kk=n(Zw,"tf.keras.Model"),Zw.forEach(t),Hk=n(xh,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),xh.forEach(t),Qk=h(Ut),w(os.$$.fragment,Ut),Vk=h(Ut),vt=a(Ut,"DIV",{class:!0});var lo=i(vt);w(qa.$$.fragment,lo),Gk=h(lo),Vo=a(lo,"P",{});var pl=i(Vo);Jk=n(pl,"The "),Si=a(pl,"A",{href:!0});var e2=i(Si);Yk=n(e2,"TFMobileBertForSequenceClassification"),e2.forEach(t),Xk=n(pl," forward method, overrides the "),Wd=a(pl,"CODE",{});var t2=i(Wd);Zk=n(t2,"__call__"),t2.forEach(t),ev=n(pl," special method."),pl.forEach(t),tv=h(lo),w(ns.$$.fragment,lo),ov=h(lo),w(ss.$$.fragment,lo),nv=h(lo),w(rs.$$.fragment,lo),lo.forEach(t),Ut.forEach(t),kp=h(s),Go=a(s,"H2",{class:!0});var zh=i(Go);as=a(zh,"A",{id:!0,class:!0,href:!0});var o2=i(as);Rd=a(o2,"SPAN",{});var n2=i(Rd);w(Pa.$$.fragment,n2),n2.forEach(t),o2.forEach(t),sv=h(zh),Ud=a(zh,"SPAN",{});var s2=i(Ud);rv=n(s2,"TFMobileBertForMultipleChoice"),s2.forEach(t),zh.forEach(t),vp=h(s),at=a(s,"DIV",{class:!0});var Kt=i(at);w(ja.$$.fragment,Kt),av=h(Kt),Kd=a(Kt,"P",{});var r2=i(Kd);iv=n(r2,`MobileBert 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.`),r2.forEach(t),lv=h(Kt),Ca=a(Kt,"P",{});var qh=i(Ca);dv=n(qh,"This model inherits from "),Ii=a(qh,"A",{href:!0});var a2=i(Ii);cv=n(a2,"TFPreTrainedModel"),a2.forEach(t),pv=n(qh,`. 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.)`),qh.forEach(t),hv=h(Kt),Na=a(Kt,"P",{});var Ph=i(Na);mv=n(Ph,"This model is also a "),Aa=a(Ph,"A",{href:!0,rel:!0});var i2=i(Aa);uv=n(i2,"tf.keras.Model"),i2.forEach(t),fv=n(Ph,` 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.`),Ph.forEach(t),gv=h(Kt),w(is.$$.fragment,Kt),_v=h(Kt),Dt=a(Kt,"DIV",{class:!0});var Ps=i(Dt);w(Oa.$$.fragment,Ps),bv=h(Ps),Jo=a(Ps,"P",{});var hl=i(Jo);kv=n(hl,"The "),Li=a(hl,"A",{href:!0});var l2=i(Li);vv=n(l2,"TFMobileBertForMultipleChoice"),l2.forEach(t),Tv=n(hl," forward method, overrides the "),Hd=a(hl,"CODE",{});var d2=i(Hd);yv=n(d2,"__call__"),d2.forEach(t),wv=n(hl," special method."),hl.forEach(t),$v=h(Ps),w(ls.$$.fragment,Ps),Mv=h(Ps),w(ds.$$.fragment,Ps),Ps.forEach(t),Kt.forEach(t),Tp=h(s),Yo=a(s,"H2",{class:!0});var jh=i(Yo);cs=a(jh,"A",{id:!0,class:!0,href:!0});var c2=i(cs);Qd=a(c2,"SPAN",{});var p2=i(Qd);w(Da.$$.fragment,p2),p2.forEach(t),c2.forEach(t),Fv=h(jh),Vd=a(jh,"SPAN",{});var h2=i(Vd);Ev=n(h2,"TFMobileBertForTokenClassification"),h2.forEach(t),jh.forEach(t),yp=h(s),it=a(s,"DIV",{class:!0});var Ht=i(it);w(Sa.$$.fragment,Ht),Bv=h(Ht),Gd=a(Ht,"P",{});var m2=i(Gd);xv=n(m2,`MobileBert 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.`),m2.forEach(t),zv=h(Ht),Ia=a(Ht,"P",{});var Ch=i(Ia);qv=n(Ch,"This model inherits from "),Wi=a(Ch,"A",{href:!0});var u2=i(Wi);Pv=n(u2,"TFPreTrainedModel"),u2.forEach(t),jv=n(Ch,`. 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.)`),Ch.forEach(t),Cv=h(Ht),La=a(Ht,"P",{});var Nh=i(La);Nv=n(Nh,"This model is also a "),Wa=a(Nh,"A",{href:!0,rel:!0});var f2=i(Wa);Av=n(f2,"tf.keras.Model"),f2.forEach(t),Ov=n(Nh,` 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.`),Nh.forEach(t),Dv=h(Ht),w(ps.$$.fragment,Ht),Sv=h(Ht),Tt=a(Ht,"DIV",{class:!0});var co=i(Tt);w(Ra.$$.fragment,co),Iv=h(co),Xo=a(co,"P",{});var ml=i(Xo);Lv=n(ml,"The "),Ri=a(ml,"A",{href:!0});var g2=i(Ri);Wv=n(g2,"TFMobileBertForTokenClassification"),g2.forEach(t),Rv=n(ml," forward method, overrides the "),Jd=a(ml,"CODE",{});var _2=i(Jd);Uv=n(_2,"__call__"),_2.forEach(t),Kv=n(ml," special method."),ml.forEach(t),Hv=h(co),w(hs.$$.fragment,co),Qv=h(co),w(ms.$$.fragment,co),Vv=h(co),w(us.$$.fragment,co),co.forEach(t),Ht.forEach(t),wp=h(s),Zo=a(s,"H2",{class:!0});var Ah=i(Zo);fs=a(Ah,"A",{id:!0,class:!0,href:!0});var b2=i(fs);Yd=a(b2,"SPAN",{});var k2=i(Yd);w(Ua.$$.fragment,k2),k2.forEach(t),b2.forEach(t),Gv=h(Ah),Xd=a(Ah,"SPAN",{});var v2=i(Xd);Jv=n(v2,"TFMobileBertForQuestionAnswering"),v2.forEach(t),Ah.forEach(t),$p=h(s),lt=a(s,"DIV",{class:!0});var Qt=i(lt);w(Ka.$$.fragment,Qt),Yv=h(Qt),en=a(Qt,"P",{});var ul=i(en);Xv=n(ul,`MobileBert 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 `),Zd=a(ul,"CODE",{});var T2=i(Zd);Zv=n(T2,"span start logits"),T2.forEach(t),eT=n(ul," and "),ec=a(ul,"CODE",{});var y2=i(ec);tT=n(y2,"span end logits"),y2.forEach(t),oT=n(ul,")."),ul.forEach(t),nT=h(Qt),Ha=a(Qt,"P",{});var Oh=i(Ha);sT=n(Oh,"This model inherits from "),Ui=a(Oh,"A",{href:!0});var w2=i(Ui);rT=n(w2,"TFPreTrainedModel"),w2.forEach(t),aT=n(Oh,`. 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.)`),Oh.forEach(t),iT=h(Qt),Qa=a(Qt,"P",{});var Dh=i(Qa);lT=n(Dh,"This model is also a "),Va=a(Dh,"A",{href:!0,rel:!0});var $2=i(Va);dT=n($2,"tf.keras.Model"),$2.forEach(t),cT=n(Dh,` subclass. Use it
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outputs"},{local:"transformers.MobileBertModel",title:"MobileBertModel"},{local:"transformers.MobileBertForPreTraining",title:"MobileBertForPreTraining"},{local:"transformers.MobileBertForMaskedLM",title:"MobileBertForMaskedLM"},{local:"transformers.MobileBertForNextSentencePrediction",title:"MobileBertForNextSentencePrediction"},{local:"transformers.MobileBertForSequenceClassification",title:"MobileBertForSequenceClassification"},{local:"transformers.MobileBertForMultipleChoice",title:"MobileBertForMultipleChoice"},{local:"transformers.MobileBertForTokenClassification",title:"MobileBertForTokenClassification"},{local:"transformers.MobileBertForQuestionAnswering",title:"MobileBertForQuestionAnswering"},{local:"transformers.TFMobileBertModel",title:"TFMobileBertModel"},{local:"transformers.TFMobileBertForPreTraining",title:"TFMobileBertForPreTraining"},{local:"transformers.TFMobileBertForMaskedLM",title:"TFMobileBertForMaskedLM"},{local:"transformers.TFMobileBertForNextSentencePrediction",title:"TFMobileBertForNextSentencePrediction"},{local:"transformers.TFMobileBertForSequenceClassification",title:"TFMobileBertForSequenceClassification"},{local:"transformers.TFMobileBertForMultipleChoice",title:"TFMobileBertForMultipleChoice"},{local:"transformers.TFMobileBertForTokenClassification",title:"TFMobileBertForTokenClassification"},{local:"transformers.TFMobileBertForQuestionAnswering",title:"TFMobileBertForQuestionAnswering"}],title:"MobileBERT"};function B1(q){return q2(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class N1 extends E2{constructor(d){super();B2(this,d,B1,F1,x2,{})}}export{N1 as default,E1 as metadata};
