import{S as bv,i as kv,s as wv,e as a,k as p,w as v,t as n,M as vv,c as r,d as t,m as h,a as i,x as T,h as s,b as m,G as e,g as b,y,q as $,o as D,B as E,v as Tv,L as be}from"../../chunks/vendor-hf-doc-builder.js";import{T as rt}from"../../chunks/Tip-hf-doc-builder.js";import{D as te}from"../../chunks/Docstring-hf-doc-builder.js";import{C as ke}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as ot}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as _e}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";import{P as Gc}from"../../chunks/PipelineTag-hf-doc-builder.js";function yv(x){let d,_,c,u,k;return u=new ke({props:{code:`from transformers import DebertaConfig, DebertaModel

# Initializing a DeBERTa microsoft/deberta-base style configuration
configuration = DebertaConfig()

# Initializing a model (with random weights) from the microsoft/deberta-base style configuration
model = DebertaModel(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> DebertaConfig, DebertaModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a DeBERTa microsoft/deberta-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = DebertaConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the microsoft/deberta-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DebertaModel(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=a("p"),_=n("Example:"),c=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Example:"),f.forEach(t),c=h(l),T(u.$$.fragment,l)},m(l,f){b(l,d,f),e(d,_),b(l,c,f),y(u,l,f),k=!0},p:be,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(c),E(u,l)}}}function $v(x){let d,_,c,u,k;return u=new ke({props:{code:`from transformers import DebertaTokenizer
tokenizer = DebertaTokenizer.from_pretrained("microsoft/deberta-base")
tokenizer("Hello world")['input_ids']
tokenizer(" Hello world")['input_ids']`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt;</span> <span class="language-python"><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DebertaTokenizer</span>
<span class="hljs-meta">&gt;&gt;&gt;</span> <span class="language-python">tokenizer = DebertaTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/deberta-base&quot;</span>)</span>
<span class="hljs-meta">&gt;&gt;&gt;</span> <span class="language-python">tokenizer(<span class="hljs-string">&quot;Hello world&quot;</span>)[<span class="hljs-string">&#x27;input_ids&#x27;</span>]</span>
[15496, 995]
<span class="hljs-meta">&gt;&gt;&gt;</span> <span class="language-python">tokenizer(<span class="hljs-string">&quot; Hello world&quot;</span>)[<span class="hljs-string">&#x27;input_ids&#x27;</span>]</span>
[18435, 995]`}}),{c(){d=a("p"),_=n("be encoded differently whether it is at the beginning of the sentence (without space) or not:"),c=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"be encoded differently whether it is at the beginning of the sentence (without space) or not:"),f.forEach(t),c=h(l),T(u.$$.fragment,l)},m(l,f){b(l,d,f),e(d,_),b(l,c,f),y(u,l,f),k=!0},p:be,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(c),E(u,l)}}}function Dv(x){let d,_,c,u,k;return{c(){d=a("p"),_=n("When used with "),c=a("code"),u=n("is_split_into_words=True"),k=n(", this tokenizer will add a space before each word (even the first one).")},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"When used with "),c=r(f,"CODE",{});var z=i(c);u=s(z,"is_split_into_words=True"),z.forEach(t),k=s(f,", this tokenizer will add a space before each word (even the first one)."),f.forEach(t)},m(l,f){b(l,d,f),e(d,_),e(d,c),e(c,u),e(d,k)},d(l){l&&t(d)}}}function Ev(x){let d,_,c,u,k;return u=new ke({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=a("p"),_=n("sequence pair mask has the following format:"),c=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"sequence pair mask has the following format:"),f.forEach(t),c=h(l),T(u.$$.fragment,l)},m(l,f){b(l,d,f),e(d,_),b(l,c,f),y(u,l,f),k=!0},p:be,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(c),E(u,l)}}}function Fv(x){let d,_,c,u,k;return u=new ke({props:{code:`from transformers import DebertaTokenizerFast
tokenizer = DebertaTokenizerFast.from_pretrained("microsoft/deberta-base")
tokenizer("Hello world")['input_ids']
tokenizer(" Hello world")['input_ids']`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt;</span> <span class="language-python"><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DebertaTokenizerFast</span>
<span class="hljs-meta">&gt;&gt;&gt;</span> <span class="language-python">tokenizer = DebertaTokenizerFast.from_pretrained(<span class="hljs-string">&quot;microsoft/deberta-base&quot;</span>)</span>
<span class="hljs-meta">&gt;&gt;&gt;</span> <span class="language-python">tokenizer(<span class="hljs-string">&quot;Hello world&quot;</span>)[<span class="hljs-string">&#x27;input_ids&#x27;</span>]</span>
[15496, 995]
<span class="hljs-meta">&gt;&gt;&gt;</span> <span class="language-python">tokenizer(<span class="hljs-string">&quot; Hello world&quot;</span>)[<span class="hljs-string">&#x27;input_ids&#x27;</span>]</span>
[18435, 995]`}}),{c(){d=a("p"),_=n("be encoded differently whether it is at the beginning of the sentence (without space) or not:"),c=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"be encoded differently whether it is at the beginning of the sentence (without space) or not:"),f.forEach(t),c=h(l),T(u.$$.fragment,l)},m(l,f){b(l,d,f),e(d,_),b(l,c,f),y(u,l,f),k=!0},p:be,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(c),E(u,l)}}}function xv(x){let d,_,c,u,k,l,f,z;return{c(){d=a("p"),_=n("When used with "),c=a("code"),u=n("is_split_into_words=True"),k=n(", this tokenizer needs to be instantiated with "),l=a("code"),f=n("add_prefix_space=True"),z=n(".")},l(ae){d=r(ae,"P",{});var V=i(d);_=s(V,"When used with "),c=r(V,"CODE",{});var B=i(c);u=s(B,"is_split_into_words=True"),B.forEach(t),k=s(V,", this tokenizer needs to be instantiated with "),l=r(V,"CODE",{});var oe=i(l);f=s(oe,"add_prefix_space=True"),oe.forEach(t),z=s(V,"."),V.forEach(t)},m(ae,V){b(ae,d,V),e(d,_),e(d,c),e(c,u),e(d,k),e(d,l),e(l,f),e(d,z)},d(ae){ae&&t(d)}}}function qv(x){let d,_,c,u,k;return u=new ke({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=a("p"),_=n("sequence pair mask has the following format:"),c=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"sequence pair mask has the following format:"),f.forEach(t),c=h(l),T(u.$$.fragment,l)},m(l,f){b(l,d,f),e(d,_),b(l,c,f),y(u,l,f),k=!0},p:be,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(c),E(u,l)}}}function zv(x){let d,_,c,u,k;return{c(){d=a("p"),_=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),c=a("code"),u=n("Module"),k=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),c=r(f,"CODE",{});var z=i(c);u=s(z,"Module"),z.forEach(t),k=s(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){b(l,d,f),e(d,_),e(d,c),e(c,u),e(d,k)},d(l){l&&t(d)}}}function jv(x){let d,_,c,u,k;return u=new ke({props:{code:`from transformers import DebertaTokenizer, DebertaModel
import torch

tokenizer = DebertaTokenizer.from_pretrained("microsoft/deberta-base")
model = DebertaModel.from_pretrained("microsoft/deberta-base")

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> DebertaTokenizer, DebertaModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = DebertaTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/deberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DebertaModel.from_pretrained(<span class="hljs-string">&quot;microsoft/deberta-base&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=a("p"),_=n("Example:"),c=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Example:"),f.forEach(t),c=h(l),T(u.$$.fragment,l)},m(l,f){b(l,d,f),e(d,_),b(l,c,f),y(u,l,f),k=!0},p:be,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(c),E(u,l)}}}function Mv(x){let d,_,c,u,k;return{c(){d=a("p"),_=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),c=a("code"),u=n("Module"),k=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),c=r(f,"CODE",{});var z=i(c);u=s(z,"Module"),z.forEach(t),k=s(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){b(l,d,f),e(d,_),e(d,c),e(c,u),e(d,k)},d(l){l&&t(d)}}}function Cv(x){let d,_,c,u,k;return u=new ke({props:{code:`from transformers import DebertaTokenizer, DebertaForMaskedLM
import torch

tokenizer = DebertaTokenizer.from_pretrained("lsanochkin/deberta-large-feedback")
model = DebertaForMaskedLM.from_pretrained("lsanochkin/deberta-large-feedback")

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)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DebertaTokenizer, DebertaForMaskedLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = DebertaTokenizer.from_pretrained(<span class="hljs-string">&quot;lsanochkin/deberta-large-feedback&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DebertaForMaskedLM.from_pretrained(<span class="hljs-string">&quot;lsanochkin/deberta-large-feedback&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>`}}),{c(){d=a("p"),_=n("Example:"),c=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Example:"),f.forEach(t),c=h(l),T(u.$$.fragment,l)},m(l,f){b(l,d,f),e(d,_),b(l,c,f),y(u,l,f),k=!0},p:be,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(c),E(u,l)}}}function Pv(x){let d,_;return d=new ke({props:{code:`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>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">0.54</span>`}}),{c(){v(d.$$.fragment)},l(c){T(d.$$.fragment,c)},m(c,u){y(d,c,u),_=!0},p:be,i(c){_||($(d.$$.fragment,c),_=!0)},o(c){D(d.$$.fragment,c),_=!1},d(c){E(d,c)}}}function Av(x){let d,_,c,u,k;return{c(){d=a("p"),_=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),c=a("code"),u=n("Module"),k=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),c=r(f,"CODE",{});var z=i(c);u=s(z,"Module"),z.forEach(t),k=s(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){b(l,d,f),e(d,_),e(d,c),e(c,u),e(d,k)},d(l){l&&t(d)}}}function Lv(x){let d,_,c,u,k;return u=new ke({props:{code:`import torch
from transformers import DebertaTokenizer, DebertaForSequenceClassification

tokenizer = DebertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-deberta")
model = DebertaForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-deberta")

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]`,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> DebertaTokenizer, DebertaForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = DebertaTokenizer.from_pretrained(<span class="hljs-string">&quot;hf-internal-testing/tiny-random-deberta&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DebertaForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;hf-internal-testing/tiny-random-deberta&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;LABEL_0&#x27;</span>`}}),{c(){d=a("p"),_=n("Example of single-label classification:"),c=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Example of single-label classification:"),f.forEach(t),c=h(l),T(u.$$.fragment,l)},m(l,f){b(l,d,f),e(d,_),b(l,c,f),y(u,l,f),k=!0},p:be,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(c),E(u,l)}}}function Bv(x){let d,_;return d=new ke({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 = DebertaForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-deberta", num_labels=num_labels)\n\nlabels = torch.tensor([1])\nloss = model(**inputs, labels=labels).loss\nround(loss.item(), 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 = DebertaForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;hf-internal-testing/tiny-random-deberta&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">0.69</span>`}}),{c(){v(d.$$.fragment)},l(c){T(d.$$.fragment,c)},m(c,u){y(d,c,u),_=!0},p:be,i(c){_||($(d.$$.fragment,c),_=!0)},o(c){D(d.$$.fragment,c),_=!1},d(c){E(d,c)}}}function Rv(x){let d,_,c,u,k;return u=new ke({props:{code:`import torch
from transformers import DebertaTokenizer, DebertaForSequenceClassification

tokenizer = DebertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-deberta")
model = DebertaForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-deberta", 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_id = logits.argmax().item()
model.config.id2label[predicted_class_id]`,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> DebertaTokenizer, DebertaForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = DebertaTokenizer.from_pretrained(<span class="hljs-string">&quot;hf-internal-testing/tiny-random-deberta&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DebertaForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;hf-internal-testing/tiny-random-deberta&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_id = logits.argmax().item()
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.id2label[predicted_class_id]
<span class="hljs-string">&#x27;LABEL_0&#x27;</span>`}}),{c(){d=a("p"),_=n("Example of multi-label classification:"),c=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Example of multi-label classification:"),f.forEach(t),c=h(l),T(u.$$.fragment,l)},m(l,f){b(l,d,f),e(d,_),b(l,c,f),y(u,l,f),k=!0},p:be,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(c),E(u,l)}}}function Iv(x){let d,_;return d=new ke({props:{code:`# 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 = DebertaForSequenceClassification.from_pretrained(
    "hf-internal-testing/tiny-random-deberta", num_labels=num_labels, problem_type="multi_label_classification"
)

labels = torch.nn.functional.one_hot(torch.tensor([predicted_class_id]), num_classes=num_labels).to(
    torch.float
)
loss = model(**inputs, labels=labels).loss
loss.backward()`,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 = DebertaForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;hf-internal-testing/tiny-random-deberta&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.nn.functional.one_hot(torch.tensor([predicted_class_id]), num_classes=num_labels).to(
<span class="hljs-meta">... </span>    torch.<span class="hljs-built_in">float</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss
<span class="hljs-meta">&gt;&gt;&gt; </span>loss.backward()`}}),{c(){v(d.$$.fragment)},l(c){T(d.$$.fragment,c)},m(c,u){y(d,c,u),_=!0},p:be,i(c){_||($(d.$$.fragment,c),_=!0)},o(c){D(d.$$.fragment,c),_=!1},d(c){E(d,c)}}}function Sv(x){let d,_,c,u,k;return{c(){d=a("p"),_=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),c=a("code"),u=n("Module"),k=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),c=r(f,"CODE",{});var z=i(c);u=s(z,"Module"),z.forEach(t),k=s(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){b(l,d,f),e(d,_),e(d,c),e(c,u),e(d,k)},d(l){l&&t(d)}}}function Ov(x){let d,_,c,u,k;return u=new ke({props:{code:`from transformers import DebertaTokenizer, DebertaForTokenClassification
import torch

tokenizer = DebertaTokenizer.from_pretrained("dbsamu/deberta-base-finetuned-ner")
model = DebertaForTokenClassification.from_pretrained("dbsamu/deberta-base-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`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DebertaTokenizer, DebertaForTokenClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = DebertaTokenizer.from_pretrained(<span class="hljs-string">&quot;dbsamu/deberta-base-finetuned-ner&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DebertaForTokenClassification.from_pretrained(<span class="hljs-string">&quot;dbsamu/deberta-base-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;LABEL_0&#x27;</span>, <span class="hljs-string">&#x27;LABEL_0&#x27;</span>, <span class="hljs-string">&#x27;LABEL_0&#x27;</span>, <span class="hljs-string">&#x27;LABEL_0&#x27;</span>, <span class="hljs-string">&#x27;LABEL_0&#x27;</span>, <span class="hljs-string">&#x27;LABEL_0&#x27;</span>, <span class="hljs-string">&#x27;LABEL_0&#x27;</span>, <span class="hljs-string">&#x27;LABEL_0&#x27;</span>, <span class="hljs-string">&#x27;LABEL_0&#x27;</span>, <span class="hljs-string">&#x27;LABEL_0&#x27;</span>, <span class="hljs-string">&#x27;LABEL_0&#x27;</span>, <span class="hljs-string">&#x27;LABEL_0&#x27;</span>]`}}),{c(){d=a("p"),_=n("Example:"),c=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Example:"),f.forEach(t),c=h(l),T(u.$$.fragment,l)},m(l,f){b(l,d,f),e(d,_),b(l,c,f),y(u,l,f),k=!0},p:be,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(c),E(u,l)}}}function Nv(x){let d,_;return d=new ke({props:{code:`labels = predicted_token_class_ids
loss = model(**inputs, labels=labels).loss
round(loss.item(), 2)`,highlighted:`<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">0.04</span>`}}),{c(){v(d.$$.fragment)},l(c){T(d.$$.fragment,c)},m(c,u){y(d,c,u),_=!0},p:be,i(c){_||($(d.$$.fragment,c),_=!0)},o(c){D(d.$$.fragment,c),_=!1},d(c){E(d,c)}}}function Wv(x){let d,_,c,u,k;return{c(){d=a("p"),_=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),c=a("code"),u=n("Module"),k=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),c=r(f,"CODE",{});var z=i(c);u=s(z,"Module"),z.forEach(t),k=s(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){b(l,d,f),e(d,_),e(d,c),e(c,u),e(d,k)},d(l){l&&t(d)}}}function Hv(x){let d,_,c,u,k;return u=new ke({props:{code:`from transformers import DebertaTokenizer, DebertaForQuestionAnswering
import torch

tokenizer = DebertaTokenizer.from_pretrained("Palak/microsoft_deberta-large_squad")
model = DebertaForQuestionAnswering.from_pretrained("Palak/microsoft_deberta-large_squad")

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)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DebertaTokenizer, DebertaForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = DebertaTokenizer.from_pretrained(<span class="hljs-string">&quot;Palak/microsoft_deberta-large_squad&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DebertaForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;Palak/microsoft_deberta-large_squad&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>`}}),{c(){d=a("p"),_=n("Example:"),c=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Example:"),f.forEach(t),c=h(l),T(u.$$.fragment,l)},m(l,f){b(l,d,f),e(d,_),b(l,c,f),y(u,l,f),k=!0},p:be,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(c),E(u,l)}}}function Uv(x){let d,_;return d=new ke({props:{code:`# target is "nice puppet"
target_start_index = torch.tensor([12])
target_end_index = torch.tensor([14])

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-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">14</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">0.14</span>`}}),{c(){v(d.$$.fragment)},l(c){T(d.$$.fragment,c)},m(c,u){y(d,c,u),_=!0},p:be,i(c){_||($(d.$$.fragment,c),_=!0)},o(c){D(d.$$.fragment,c),_=!1},d(c){E(d,c)}}}function Qv(x){let d,_,c,u,k,l,f,z,ae,V,B,oe,se,F,Ee,G,Fe,we,S,xe,le,J,qe,ve,Q,ze,Te,K,je,ye,ne,M,C,de,X,Me,$e,R,Ce,ce,P,Pe,W,Ae,Le,H,Be,Re,I,re,O,me,Z,A,ue,L,De,fe;return{c(){d=a("p"),_=n("TensorFlow models and layers in "),c=a("code"),u=n("transformers"),k=n(" accept two formats as input:"),l=p(),f=a("ul"),z=a("li"),ae=n("having all inputs as keyword arguments (like PyTorch models), or"),V=p(),B=a("li"),oe=n("having all inputs as a list, tuple or dict in the first positional argument."),se=p(),F=a("p"),Ee=n(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),G=a("code"),Fe=n("model.fit()"),we=n(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),S=a("code"),xe=n("model.fit()"),le=n(` supports! If, however, you want to use the second
format outside of Keras methods like `),J=a("code"),qe=n("fit()"),ve=n(" and "),Q=a("code"),ze=n("predict()"),Te=n(`, such as when creating your own layers or models with
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positional argument:`),ne=p(),M=a("ul"),C=a("li"),de=n("a single Tensor with "),X=a("code"),Me=n("input_ids"),$e=n(" only and nothing else: "),R=a("code"),Ce=n("model(input_ids)"),ce=p(),P=a("li"),Pe=n(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),W=a("code"),Ae=n("model([input_ids, attention_mask])"),Le=n(" or "),H=a("code"),Be=n("model([input_ids, attention_mask, token_type_ids])"),Re=p(),I=a("li"),re=n(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
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pass your inputs and labels in any format that `),S=r(j,"CODE",{});var Ke=i(S);xe=s(Ke,"model.fit()"),Ke.forEach(t),le=s(j,` supports! If, however, you want to use the second
format outside of Keras methods like `),J=r(j,"CODE",{});var Ge=i(J);qe=s(Ge,"fit()"),Ge.forEach(t),ve=s(j," and "),Q=r(j,"CODE",{});var Ve=i(Q);ze=s(Ve,"predict()"),Ve.forEach(t),Te=s(j,`, such as when creating your own layers or models with
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positional argument:`),j.forEach(t),ne=h(w),M=r(w,"UL",{});var N=i(M);C=r(N,"LI",{});var U=i(C);de=s(U,"a single Tensor with "),X=r(U,"CODE",{});var Je=i(X);Me=s(Je,"input_ids"),Je.forEach(t),$e=s(U," only and nothing else: "),R=r(U,"CODE",{});var Xe=i(R);Ce=s(Xe,"model(input_ids)"),Xe.forEach(t),U.forEach(t),ce=h(N),P=r(N,"LI",{});var Y=i(P);Pe=s(Y,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),W=r(Y,"CODE",{});var ge=i(W);Ae=s(ge,"model([input_ids, attention_mask])"),ge.forEach(t),Le=s(Y," or "),H=r(Y,"CODE",{});var Qe=i(H);Be=s(Qe,"model([input_ids, attention_mask, token_type_ids])"),Qe.forEach(t),Y.forEach(t),Re=h(N),I=r(N,"LI",{});var Ie=i(I);re=s(Ie,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),O=r(Ie,"CODE",{});var Ye=i(O);me=s(Ye,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),Ye.forEach(t),Ie.forEach(t),N.forEach(t),Z=h(w),A=r(w,"P",{});var ee=i(A);ue=s(ee,`Note that when creating models and layers with
`),L=r(ee,"A",{href:!0,rel:!0});var Ze=i(L);De=s(Ze,"subclassing"),Ze.forEach(t),fe=s(ee,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),ee.forEach(t),this.h()},h(){m(L,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),m(L,"rel","nofollow")},m(w,q){b(w,d,q),e(d,_),e(d,c),e(c,u),e(d,k),b(w,l,q),b(w,f,q),e(f,z),e(z,ae),e(f,V),e(f,B),e(B,oe),b(w,se,q),b(w,F,q),e(F,Ee),e(F,G),e(G,Fe),e(F,we),e(F,S),e(S,xe),e(F,le),e(F,J),e(J,qe),e(F,ve),e(F,Q),e(Q,ze),e(F,Te),e(F,K),e(K,je),e(F,ye),b(w,ne,q),b(w,M,q),e(M,C),e(C,de),e(C,X),e(X,Me),e(C,$e),e(C,R),e(R,Ce),e(M,ce),e(M,P),e(P,Pe),e(P,W),e(W,Ae),e(P,Le),e(P,H),e(H,Be),e(M,Re),e(M,I),e(I,re),e(I,O),e(O,me),b(w,Z,q),b(w,A,q),e(A,ue),e(A,L),e(L,De),e(A,fe)},d(w){w&&t(d),w&&t(l),w&&t(f),w&&t(se),w&&t(F),w&&t(ne),w&&t(M),w&&t(Z),w&&t(A)}}}function Kv(x){let d,_,c,u,k;return{c(){d=a("p"),_=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),c=a("code"),u=n("Module"),k=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),c=r(f,"CODE",{});var z=i(c);u=s(z,"Module"),z.forEach(t),k=s(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){b(l,d,f),e(d,_),e(d,c),e(c,u),e(d,k)},d(l){l&&t(d)}}}function Gv(x){let d,_,c,u,k;return u=new ke({props:{code:`from transformers import DebertaTokenizer, TFDebertaModel
import tensorflow as tf

tokenizer = DebertaTokenizer.from_pretrained("kamalkraj/deberta-base")
model = TFDebertaModel.from_pretrained("kamalkraj/deberta-base")

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> DebertaTokenizer, TFDebertaModel
<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 = DebertaTokenizer.from_pretrained(<span class="hljs-string">&quot;kamalkraj/deberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDebertaModel.from_pretrained(<span class="hljs-string">&quot;kamalkraj/deberta-base&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=a("p"),_=n("Example:"),c=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Example:"),f.forEach(t),c=h(l),T(u.$$.fragment,l)},m(l,f){b(l,d,f),e(d,_),b(l,c,f),y(u,l,f),k=!0},p:be,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(c),E(u,l)}}}function Vv(x){let d,_,c,u,k,l,f,z,ae,V,B,oe,se,F,Ee,G,Fe,we,S,xe,le,J,qe,ve,Q,ze,Te,K,je,ye,ne,M,C,de,X,Me,$e,R,Ce,ce,P,Pe,W,Ae,Le,H,Be,Re,I,re,O,me,Z,A,ue,L,De,fe;return{c(){d=a("p"),_=n("TensorFlow models and layers in "),c=a("code"),u=n("transformers"),k=n(" accept two formats as input:"),l=p(),f=a("ul"),z=a("li"),ae=n("having all inputs as keyword arguments (like PyTorch models), or"),V=p(),B=a("li"),oe=n("having all inputs as a list, tuple or dict in the first positional argument."),se=p(),F=a("p"),Ee=n(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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pass your inputs and labels in any format that `),S=r(j,"CODE",{});var Ke=i(S);xe=s(Ke,"model.fit()"),Ke.forEach(t),le=s(j,` supports! If, however, you want to use the second
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`),O=r(Ie,"CODE",{});var Ye=i(O);me=s(Ye,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),Ye.forEach(t),Ie.forEach(t),N.forEach(t),Z=h(w),A=r(w,"P",{});var ee=i(A);ue=s(ee,`Note that when creating models and layers with
`),L=r(ee,"A",{href:!0,rel:!0});var Ze=i(L);De=s(Ze,"subclassing"),Ze.forEach(t),fe=s(ee,` then you don\u2019t need to worry
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instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),c=r(f,"CODE",{});var z=i(c);u=s(z,"Module"),z.forEach(t),k=s(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){b(l,d,f),e(d,_),e(d,c),e(c,u),e(d,k)},d(l){l&&t(d)}}}function Xv(x){let d,_,c,u,k;return u=new ke({props:{code:`from transformers import DebertaTokenizer, TFDebertaForMaskedLM
import tensorflow as tf

tokenizer = DebertaTokenizer.from_pretrained("kamalkraj/deberta-base")
model = TFDebertaForMaskedLM.from_pretrained("kamalkraj/deberta-base")

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

tokenizer = DebertaTokenizer.from_pretrained("kamalkraj/deberta-base")
model = TFDebertaForSequenceClassification.from_pretrained("kamalkraj/deberta-base")

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> DebertaTokenizer, TFDebertaForSequenceClassification
<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 = DebertaTokenizer.from_pretrained(<span class="hljs-string">&quot;kamalkraj/deberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDebertaForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;kamalkraj/deberta-base&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]
`}}),{c(){d=a("p"),_=n("Example:"),c=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Example:"),f.forEach(t),c=h(l),T(u.$$.fragment,l)},m(l,f){b(l,d,f),e(d,_),b(l,c,f),y(u,l,f),k=!0},p:be,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(c),E(u,l)}}}function oT(x){let d,_;return d=new ke({props:{code:`# 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 = TFDebertaForSequenceClassification.from_pretrained("kamalkraj/deberta-base", num_labels=num_labels)

labels = tf.constant(1)
loss = model(**inputs, labels=labels).loss
round(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 = TFDebertaForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;kamalkraj/deberta-base&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>)
`}}),{c(){v(d.$$.fragment)},l(c){T(d.$$.fragment,c)},m(c,u){y(d,c,u),_=!0},p:be,i(c){_||($(d.$$.fragment,c),_=!0)},o(c){D(d.$$.fragment,c),_=!1},d(c){E(d,c)}}}function nT(x){let d,_,c,u,k,l,f,z,ae,V,B,oe,se,F,Ee,G,Fe,we,S,xe,le,J,qe,ve,Q,ze,Te,K,je,ye,ne,M,C,de,X,Me,$e,R,Ce,ce,P,Pe,W,Ae,Le,H,Be,Re,I,re,O,me,Z,A,ue,L,De,fe;return{c(){d=a("p"),_=n("TensorFlow models and layers in "),c=a("code"),u=n("transformers"),k=n(" accept two formats as input:"),l=p(),f=a("ul"),z=a("li"),ae=n("having all inputs as keyword arguments (like PyTorch models), or"),V=p(),B=a("li"),oe=n("having all inputs as a list, tuple or dict in the first positional argument."),se=p(),F=a("p"),Ee=n(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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`),L=a("a"),De=n("subclassing"),fe=n(` then you don\u2019t need to worry
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pass your inputs and labels in any format that `),S=r(j,"CODE",{});var Ke=i(S);xe=s(Ke,"model.fit()"),Ke.forEach(t),le=s(j,` supports! If, however, you want to use the second
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`),W=r(Y,"CODE",{});var ge=i(W);Ae=s(ge,"model([input_ids, attention_mask])"),ge.forEach(t),Le=s(Y," or "),H=r(Y,"CODE",{});var Qe=i(H);Be=s(Qe,"model([input_ids, attention_mask, token_type_ids])"),Qe.forEach(t),Y.forEach(t),Re=h(N),I=r(N,"LI",{});var Ie=i(I);re=s(Ie,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),O=r(Ie,"CODE",{});var Ye=i(O);me=s(Ye,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),Ye.forEach(t),Ie.forEach(t),N.forEach(t),Z=h(w),A=r(w,"P",{});var ee=i(A);ue=s(ee,`Note that when creating models and layers with
`),L=r(ee,"A",{href:!0,rel:!0});var Ze=i(L);De=s(Ze,"subclassing"),Ze.forEach(t),fe=s(ee,` 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!`),ee.forEach(t),this.h()},h(){m(L,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),m(L,"rel","nofollow")},m(w,q){b(w,d,q),e(d,_),e(d,c),e(c,u),e(d,k),b(w,l,q),b(w,f,q),e(f,z),e(z,ae),e(f,V),e(f,B),e(B,oe),b(w,se,q),b(w,F,q),e(F,Ee),e(F,G),e(G,Fe),e(F,we),e(F,S),e(S,xe),e(F,le),e(F,J),e(J,qe),e(F,ve),e(F,Q),e(Q,ze),e(F,Te),e(F,K),e(K,je),e(F,ye),b(w,ne,q),b(w,M,q),e(M,C),e(C,de),e(C,X),e(X,Me),e(C,$e),e(C,R),e(R,Ce),e(M,ce),e(M,P),e(P,Pe),e(P,W),e(W,Ae),e(P,Le),e(P,H),e(H,Be),e(M,Re),e(M,I),e(I,re),e(I,O),e(O,me),b(w,Z,q),b(w,A,q),e(A,ue),e(A,L),e(L,De),e(A,fe)},d(w){w&&t(d),w&&t(l),w&&t(f),w&&t(se),w&&t(F),w&&t(ne),w&&t(M),w&&t(Z),w&&t(A)}}}function sT(x){let d,_,c,u,k;return{c(){d=a("p"),_=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),c=a("code"),u=n("Module"),k=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),c=r(f,"CODE",{});var z=i(c);u=s(z,"Module"),z.forEach(t),k=s(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){b(l,d,f),e(d,_),e(d,c),e(c,u),e(d,k)},d(l){l&&t(d)}}}function aT(x){let d,_,c,u,k;return u=new ke({props:{code:`from transformers import DebertaTokenizer, TFDebertaForTokenClassification
import tensorflow as tf

tokenizer = DebertaTokenizer.from_pretrained("kamalkraj/deberta-base")
model = TFDebertaForTokenClassification.from_pretrained("kamalkraj/deberta-base")

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

tokenizer = DebertaTokenizer.from_pretrained("kamalkraj/deberta-base")
model = TFDebertaForQuestionAnswering.from_pretrained("kamalkraj/deberta-base")

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> DebertaTokenizer, TFDebertaForQuestionAnswering
<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 = DebertaTokenizer.from_pretrained(<span class="hljs-string">&quot;kamalkraj/deberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDebertaForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;kamalkraj/deberta-base&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)
`}}),{c(){d=a("p"),_=n("Example:"),c=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var f=i(d);_=s(f,"Example:"),f.forEach(t),c=h(l),T(u.$$.fragment,l)},m(l,f){b(l,d,f),e(d,_),b(l,c,f),y(u,l,f),k=!0},p:be,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(c),E(u,l)}}}function cT(x){let d,_;return d=new ke({props:{code:`# target is "nice puppet"
target_start_index = tf.constant([14])
target_end_index = tf.constant([15])

outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
loss = tf.math.reduce_mean(outputs.loss)
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">14</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>target_end_index = tf.constant([<span class="hljs-number">15</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = tf.math.reduce_mean(outputs.loss)
<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>)
`}}),{c(){v(d.$$.fragment)},l(c){T(d.$$.fragment,c)},m(c,u){y(d,c,u),_=!0},p:be,i(c){_||($(d.$$.fragment,c),_=!0)},o(c){D(d.$$.fragment,c),_=!1},d(c){E(d,c)}}}function pT(x){let d,_,c,u,k,l,f,z,ae,V,B,oe,se,F,Ee,G,Fe,we,S,xe,le,J,qe,ve,Q,ze,Te,K,je,ye,ne,M,C,de,X,Me,$e,R,Ce,ce,P,Pe,W,Ae,Le,H,Be,Re,I,re,O,me,Z,A,ue,L,De,fe,w,q,Oe,ie,pe,Ne,j,We,Ke,Ge,Ve,He,N,U,Je,Xe,Y,ge,Qe,Ie,Ye,ee,Ze,Vc,rs,Jc,Xc,Yc,Rt,Er,Zc,ep,is,tp,op,ls,np,sp,Td,ds,yd,$t,It,Fr,ap,rp,cs,ip,lp,ps,dp,cp,pp,St,xr,hp,fp,hs,mp,up,fs,gp,_p,bp,qr,ms,kp,wp,vp,zr,us,Tp,yp,$d,gs,Dd,Ot,Nt,jr,$p,Dp,_s,Ep,Fp,bs,xp,qp,zp,Wt,Mr,jp,Mp,ks,Cp,Pp,ws,Ap,Lp,Bp,Cr,vs,Rp,Ip,Ed,Ts,Fd,Ht,Ut,Pr,Sp,Op,ys,Np,Wp,$s,Hp,Up,Qp,Qt,Ar,Kp,Gp,Ds,Vp,Jp,Es,Xp,Yp,Zp,Lr,Fs,eh,th,xd,ho,Qo,yi,xs,oh,$i,nh,qd,wt,qs,sh,Ct,ah,Br,rh,ih,Rr,lh,dh,zs,ch,ph,hh,fo,fh,Ir,mh,uh,Sr,gh,_h,bh,Ko,zd,mo,Go,Di,js,kh,Ei,wh,jd,he,Ms,vh,Fi,Th,yh,xi,$h,Dh,Vo,Eh,Cs,Fh,qi,xh,qh,zh,Jo,jh,Ps,Mh,Or,Ch,Ph,Ah,Kt,As,Lh,zi,Bh,Rh,Ls,ji,Ih,Sh,Mi,Oh,Nh,Xo,Bs,Wh,uo,Hh,Ci,Uh,Qh,Pi,Kh,Gh,Vh,Dt,Rs,Jh,Ai,Xh,Yh,Yo,Zh,go,ef,Li,tf,of,Bi,nf,sf,af,Nr,Is,Md,_o,Zo,Ri,Ss,rf,Ii,lf,Cd,Se,Os,df,Ns,cf,Si,pf,hf,ff,Oi,mf,uf,en,gf,Ws,_f,Ni,bf,kf,wf,tn,vf,Hs,Tf,Wr,yf,$f,Df,Gt,Us,Ef,Wi,Ff,xf,Qs,Hi,qf,zf,Ui,jf,Mf,Et,Ks,Cf,Qi,Pf,Af,on,Lf,bo,Bf,Ki,Rf,If,Gi,Sf,Of,Pd,ko,nn,Vi,Gs,Nf,Ji,Wf,Ad,vt,Vs,Hf,Js,Uf,Xs,Qf,Kf,Gf,Ys,Vf,Zs,Jf,Xf,Yf,Ft,ea,Zf,wo,em,Hr,tm,om,Xi,nm,sm,am,sn,rm,an,Ld,vo,rn,Yi,ta,im,Zi,lm,Bd,To,oa,dm,el,cm,Rd,yo,ln,tl,na,pm,ol,hm,Id,Tt,sa,fm,$o,mm,nl,um,gm,aa,_m,bm,km,ra,wm,ia,vm,Tm,ym,ht,la,$m,Do,Dm,Ur,Em,Fm,sl,xm,qm,zm,dn,jm,cn,Mm,pn,Sd,Eo,hn,al,da,Cm,rl,Pm,Od,it,ca,Am,il,Lm,Bm,pa,Rm,ha,Im,Sm,Om,fa,Nm,ma,Wm,Hm,Um,et,ua,Qm,Fo,Km,Qr,Gm,Vm,ll,Jm,Xm,Ym,fn,Zm,mn,eu,un,tu,gn,ou,_n,Nd,xo,bn,dl,ga,nu,cl,su,Wd,lt,_a,au,pl,ru,iu,ba,lu,ka,du,cu,pu,wa,hu,va,fu,mu,uu,ft,Ta,gu,qo,_u,Kr,bu,ku,hl,wu,vu,Tu,kn,yu,wn,$u,vn,Hd,zo,Tn,fl,ya,Du,ml,Eu,Ud,dt,$a,Fu,jo,xu,ul,qu,zu,gl,ju,Mu,Cu,Da,Pu,Ea,Au,Lu,Bu,Fa,Ru,xa,Iu,Su,Ou,mt,qa,Nu,Mo,Wu,Gr,Hu,Uu,_l,Qu,Ku,Gu,yn,Vu,$n,Ju,Dn,Qd,Co,En,bl,za,Xu,kl,Yu,Kd,ct,ja,Zu,Ma,eg,Ca,tg,og,ng,Pa,sg,Aa,ag,rg,ig,Fn,lg,xt,La,dg,Po,cg,Vr,pg,hg,wl,fg,mg,ug,xn,gg,qn,Gd,Ao,zn,vl,Ba,_g,Tl,bg,Vd,Pt,Ra,kg,yl,wg,vg,qt,Ia,Tg,$l,yg,$g,Sa,Dg,Dl,Eg,Fg,xg,yt,qg,El,zg,jg,Fl,Mg,Cg,xl,Pg,Ag,ql,Lg,Bg,Jd,Lo,jn,zl,Oa,Rg,jl,Ig,Xd,pt,Na,Sg,Bo,Og,Ml,Ng,Wg,Wa,Hg,Ug,Qg,Ha,Kg,Ua,Gg,Vg,Jg,Mn,Xg,ut,Qa,Yg,Ro,Zg,Jr,e_,t_,Cl,o_,n_,s_,Cn,a_,Pn,r_,An,Yd,Io,Ln,Pl,Ka,i_,Al,l_,Zd,nt,Ga,d_,Ll,c_,p_,Va,h_,Ja,f_,m_,u_,Xa,g_,Ya,__,b_,k_,Bn,w_,gt,Za,v_,So,T_,Xr,y_,$_,Bl,D_,E_,F_,Rn,x_,In,q_,Sn,ec,Oo,On,Rl,er,z_,Il,j_,tc,st,tr,M_,Sl,C_,P_,or,A_,nr,L_,B_,R_,sr,I_,ar,S_,O_,N_,Nn,W_,_t,rr,H_,No,U_,Yr,Q_,K_,Ol,G_,V_,J_,Wn,X_,Hn,Y_,Un,oc,Wo,Qn,Nl,ir,Z_,Wl,eb,nc,at,lr,tb,Ho,ob,Hl,nb,sb,Ul,ab,rb,ib,dr,lb,cr,db,cb,pb,pr,hb,hr,fb,mb,ub,Kn,gb,bt,fr,_b,Uo,bb,Zr,kb,wb,Ql,vb,Tb,yb,Gn,$b,Vn,Db,Jn,sc;return l=new ot({}),F=new ot({}),Z=new ot({}),Oe=new Gc({props:{pipeline:"text-classification"}}),ds=new Gc({props:{pipeline:"token-classification"}}),gs=new Gc({props:{pipeline:"fill-mask"}}),Ts=new Gc({props:{pipeline:"question-answering"}}),xs=new ot({}),qs=new te({props:{name:"class transformers.DebertaConfig",anchor:"transformers.DebertaConfig",parameters:[{name:"vocab_size",val:" = 50265"},{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.1"},{name:"attention_probs_dropout_prob",val:" = 0.1"},{name:"max_position_embeddings",val:" = 512"},{name:"type_vocab_size",val:" = 0"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-07"},{name:"relative_attention",val:" = False"},{name:"max_relative_positions",val:" = -1"},{name:"pad_token_id",val:" = 0"},{name:"position_biased_input",val:" = True"},{name:"pos_att_type",val:" = None"},{name:"pooler_dropout",val:" = 0"},{name:"pooler_hidden_act",val:" = 'gelu'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DebertaConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
Vocabulary size of the DeBERTa model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> or <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a>.`,name:"vocab_size"},{anchor:"transformers.DebertaConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.DebertaConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.DebertaConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.DebertaConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.DebertaConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>Callable</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code>, <code>&quot;gelu&quot;</code>, <code>&quot;tanh&quot;</code>, <code>&quot;gelu_fast&quot;</code>, <code>&quot;mish&quot;</code>, <code>&quot;linear&quot;</code>, <code>&quot;sigmoid&quot;</code> and <code>&quot;gelu_new&quot;</code>
are supported.`,name:"hidden_act"},{anchor:"transformers.DebertaConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.DebertaConfig.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.DebertaConfig.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.DebertaConfig.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/v4.25.1/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> or <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a>.`,name:"type_vocab_size"},{anchor:"transformers.DebertaConfig.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.DebertaConfig.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.DebertaConfig.relative_attention",description:`<strong>relative_attention</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether use relative position encoding.`,name:"relative_attention"},{anchor:"transformers.DebertaConfig.max_relative_positions",description:`<strong>max_relative_positions</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The range of relative positions <code>[-max_position_embeddings, max_position_embeddings]</code>. Use the same value
as <code>max_position_embeddings</code>.`,name:"max_relative_positions"},{anchor:"transformers.DebertaConfig.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
The value used to pad input_ids.`,name:"pad_token_id"},{anchor:"transformers.DebertaConfig.position_biased_input",description:`<strong>position_biased_input</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether add absolute position embedding to content embedding.`,name:"position_biased_input"},{anchor:"transformers.DebertaConfig.pos_att_type",description:`<strong>pos_att_type</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
The type of relative position attention, it can be a combination of <code>[&quot;p2c&quot;, &quot;c2p&quot;]</code>, e.g. <code>[&quot;p2c&quot;]</code>,
<code>[&quot;p2c&quot;, &quot;c2p&quot;]</code>.`,name:"pos_att_type"},{anchor:"transformers.DebertaConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, optional, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/deberta/configuration_deberta.py#L40"}}),Ko=new _e({props:{anchor:"transformers.DebertaConfig.example",$$slots:{default:[yv]},$$scope:{ctx:x}}}),js=new ot({}),Ms=new te({props:{name:"class transformers.DebertaTokenizer",anchor:"transformers.DebertaTokenizer",parameters:[{name:"vocab_file",val:""},{name:"merges_file",val:""},{name:"errors",val:" = 'replace'"},{name:"bos_token",val:" = '[CLS]'"},{name:"eos_token",val:" = '[SEP]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"unk_token",val:" = '[UNK]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"add_prefix_space",val:" = False"},{name:"add_bos_token",val:" = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DebertaTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
Path to the vocabulary file.`,name:"vocab_file"},{anchor:"transformers.DebertaTokenizer.merges_file",description:`<strong>merges_file</strong> (<code>str</code>) &#x2014;
Path to the merges file.`,name:"merges_file"},{anchor:"transformers.DebertaTokenizer.errors",description:`<strong>errors</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;replace&quot;</code>) &#x2014;
Paradigm to follow when decoding bytes to UTF-8. See
<a href="https://docs.python.org/3/library/stdtypes.html#bytes.decode" rel="nofollow">bytes.decode</a> for more information.`,name:"errors"},{anchor:"transformers.DebertaTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The beginning of sequence token.`,name:"bos_token"},{anchor:"transformers.DebertaTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The end of sequence token.`,name:"eos_token"},{anchor:"transformers.DebertaTokenizer.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.DebertaTokenizer.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.DebertaTokenizer.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.DebertaTokenizer.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.DebertaTokenizer.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.DebertaTokenizer.add_prefix_space",description:`<strong>add_prefix_space</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (Deberta tokenizer detect beginning of words by the preceding space).`,name:"add_prefix_space"},{anchor:"transformers.DebertaTokenizer.add_bos_token",description:`<strong>add_bos_token</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to add an initial &lt;|endoftext|&gt; to the input. This allows to treat the leading word just as
any other word.`,name:"add_bos_token"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/deberta/tokenization_deberta.py#L112"}}),Vo=new _e({props:{anchor:"transformers.DebertaTokenizer.example",$$slots:{default:[$v]},$$scope:{ctx:x}}}),Jo=new rt({props:{$$slots:{default:[Dv]},$$scope:{ctx:x}}}),As=new te({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.DebertaTokenizer.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.DebertaTokenizer.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.DebertaTokenizer.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/v4.25.1/src/transformers/models/deberta/tokenization_deberta.py#L289",returnDescription:`
<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),Bs=new te({props:{name:"get_special_tokens_mask",anchor:"transformers.DebertaTokenizer.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.DebertaTokenizer.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.DebertaTokenizer.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.DebertaTokenizer.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/v4.25.1/src/transformers/models/deberta/tokenization_deberta.py#L314",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>
`}}),Rs=new te({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.DebertaTokenizer.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.DebertaTokenizer.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.DebertaTokenizer.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/v4.25.1/src/transformers/models/deberta/tokenization_deberta.py#L341",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>
`}}),Yo=new _e({props:{anchor:"transformers.DebertaTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[Ev]},$$scope:{ctx:x}}}),Is=new te({props:{name:"save_vocabulary",anchor:"transformers.DebertaTokenizer.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/deberta/tokenization_deberta.py#L400"}}),Ss=new ot({}),Os=new te({props:{name:"class transformers.DebertaTokenizerFast",anchor:"transformers.DebertaTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"merges_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"errors",val:" = 'replace'"},{name:"bos_token",val:" = '[CLS]'"},{name:"eos_token",val:" = '[SEP]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"unk_token",val:" = '[UNK]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"add_prefix_space",val:" = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DebertaTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
Path to the vocabulary file.`,name:"vocab_file"},{anchor:"transformers.DebertaTokenizerFast.merges_file",description:`<strong>merges_file</strong> (<code>str</code>) &#x2014;
Path to the merges file.`,name:"merges_file"},{anchor:"transformers.DebertaTokenizerFast.tokenizer_file",description:`<strong>tokenizer_file</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The path to a tokenizer file to use instead of the vocab file.`,name:"tokenizer_file"},{anchor:"transformers.DebertaTokenizerFast.errors",description:`<strong>errors</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;replace&quot;</code>) &#x2014;
Paradigm to follow when decoding bytes to UTF-8. See
<a href="https://docs.python.org/3/library/stdtypes.html#bytes.decode" rel="nofollow">bytes.decode</a> for more information.`,name:"errors"},{anchor:"transformers.DebertaTokenizerFast.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The beginning of sequence token.`,name:"bos_token"},{anchor:"transformers.DebertaTokenizerFast.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The end of sequence token.`,name:"eos_token"},{anchor:"transformers.DebertaTokenizerFast.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.DebertaTokenizerFast.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.DebertaTokenizerFast.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.DebertaTokenizerFast.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.DebertaTokenizerFast.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.DebertaTokenizerFast.add_prefix_space",description:`<strong>add_prefix_space</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (Deberta tokenizer detect beginning of words by the preceding space).`,name:"add_prefix_space"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/deberta/tokenization_deberta_fast.py#L74"}}),en=new _e({props:{anchor:"transformers.DebertaTokenizerFast.example",$$slots:{default:[Fv]},$$scope:{ctx:x}}}),tn=new rt({props:{$$slots:{default:[xv]},$$scope:{ctx:x}}}),Us=new te({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.DebertaTokenizerFast.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.DebertaTokenizerFast.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.DebertaTokenizerFast.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/v4.25.1/src/transformers/models/deberta/tokenization_deberta_fast.py#L210",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>
`}}),Ks=new te({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.DebertaTokenizerFast.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.DebertaTokenizerFast.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.DebertaTokenizerFast.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/v4.25.1/src/transformers/models/deberta/tokenization_deberta_fast.py#L235",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>
`}}),on=new _e({props:{anchor:"transformers.DebertaTokenizerFast.create_token_type_ids_from_sequences.example",$$slots:{default:[qv]},$$scope:{ctx:x}}}),Gs=new ot({}),Vs=new te({props:{name:"class transformers.DebertaModel",anchor:"transformers.DebertaModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.DebertaModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</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/v4.25.1/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/v4.25.1/src/transformers/models/deberta/modeling_deberta.py#L927"}}),ea=new te({props:{name:"forward",anchor:"transformers.DebertaModel.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:"inputs_embeds",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.DebertaModel.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/v4.25.1/en/model_doc/deberta#transformers.DebertaTokenizer">DebertaTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.DebertaModel.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.DebertaModel.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.DebertaModel.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.DebertaModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DebertaModel.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.DebertaModel.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.DebertaModel.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/v4.25.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/deberta/modeling_deberta.py#L951",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</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/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig"
>DebertaConfig</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>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/v4.25.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),sn=new rt({props:{$$slots:{default:[zv]},$$scope:{ctx:x}}}),an=new _e({props:{anchor:"transformers.DebertaModel.forward.example",$$slots:{default:[jv]},$$scope:{ctx:x}}}),ta=new ot({}),oa=new te({props:{name:"class transformers.DebertaPreTrainedModel",anchor:"transformers.DebertaPreTrainedModel",parameters:[{name:"config",val:": PretrainedConfig"},{name:"*inputs",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/deberta/modeling_deberta.py#L832"}}),na=new ot({}),sa=new te({props:{name:"class transformers.DebertaForMaskedLM",anchor:"transformers.DebertaForMaskedLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.DebertaForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</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/v4.25.1/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/v4.25.1/src/transformers/models/deberta/modeling_deberta.py#L1039"}}),la=new te({props:{name:"forward",anchor:"transformers.DebertaForMaskedLM.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:"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.DebertaForMaskedLM.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/v4.25.1/en/model_doc/deberta#transformers.DebertaTokenizer">DebertaTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.DebertaForMaskedLM.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.DebertaForMaskedLM.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.DebertaForMaskedLM.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.DebertaForMaskedLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DebertaForMaskedLM.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.DebertaForMaskedLM.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.DebertaForMaskedLM.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/v4.25.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DebertaForMaskedLM.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/v4.25.1/src/transformers/models/deberta/modeling_deberta.py#L1058",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig"
>DebertaConfig</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/v4.25.1/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),dn=new rt({props:{$$slots:{default:[Mv]},$$scope:{ctx:x}}}),cn=new _e({props:{anchor:"transformers.DebertaForMaskedLM.forward.example",$$slots:{default:[Cv]},$$scope:{ctx:x}}}),pn=new _e({props:{anchor:"transformers.DebertaForMaskedLM.forward.example-2",$$slots:{default:[Pv]},$$scope:{ctx:x}}}),da=new ot({}),ca=new te({props:{name:"class transformers.DebertaForSequenceClassification",anchor:"transformers.DebertaForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.DebertaForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</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/v4.25.1/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/v4.25.1/src/transformers/models/deberta/modeling_deberta.py#L1177"}}),ua=new te({props:{name:"forward",anchor:"transformers.DebertaForSequenceClassification.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:"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.DebertaForSequenceClassification.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/v4.25.1/en/model_doc/deberta#transformers.DebertaTokenizer">DebertaTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.DebertaForSequenceClassification.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.DebertaForSequenceClassification.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.DebertaForSequenceClassification.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.DebertaForSequenceClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DebertaForSequenceClassification.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.DebertaForSequenceClassification.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.DebertaForSequenceClassification.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/v4.25.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DebertaForSequenceClassification.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/v4.25.1/src/transformers/models/deberta/modeling_deberta.py#L1202",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig"
>DebertaConfig</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/v4.25.1/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),fn=new rt({props:{$$slots:{default:[Av]},$$scope:{ctx:x}}}),mn=new _e({props:{anchor:"transformers.DebertaForSequenceClassification.forward.example",$$slots:{default:[Lv]},$$scope:{ctx:x}}}),un=new _e({props:{anchor:"transformers.DebertaForSequenceClassification.forward.example-2",$$slots:{default:[Bv]},$$scope:{ctx:x}}}),gn=new _e({props:{anchor:"transformers.DebertaForSequenceClassification.forward.example-3",$$slots:{default:[Rv]},$$scope:{ctx:x}}}),_n=new _e({props:{anchor:"transformers.DebertaForSequenceClassification.forward.example-4",$$slots:{default:[Iv]},$$scope:{ctx:x}}}),ga=new ot({}),_a=new te({props:{name:"class transformers.DebertaForTokenClassification",anchor:"transformers.DebertaForTokenClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.DebertaForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</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/v4.25.1/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/v4.25.1/src/transformers/models/deberta/modeling_deberta.py#L1298"}}),Ta=new te({props:{name:"forward",anchor:"transformers.DebertaForTokenClassification.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:"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.DebertaForTokenClassification.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/v4.25.1/en/model_doc/deberta#transformers.DebertaTokenizer">DebertaTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.DebertaForTokenClassification.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.DebertaForTokenClassification.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.DebertaForTokenClassification.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.DebertaForTokenClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DebertaForTokenClassification.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.DebertaForTokenClassification.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.DebertaForTokenClassification.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/v4.25.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DebertaForTokenClassification.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/v4.25.1/src/transformers/models/deberta/modeling_deberta.py#L1312",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig"
>DebertaConfig</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/v4.25.1/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),kn=new rt({props:{$$slots:{default:[Sv]},$$scope:{ctx:x}}}),wn=new _e({props:{anchor:"transformers.DebertaForTokenClassification.forward.example",$$slots:{default:[Ov]},$$scope:{ctx:x}}}),vn=new _e({props:{anchor:"transformers.DebertaForTokenClassification.forward.example-2",$$slots:{default:[Nv]},$$scope:{ctx:x}}}),ya=new ot({}),$a=new te({props:{name:"class transformers.DebertaForQuestionAnswering",anchor:"transformers.DebertaForQuestionAnswering",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.DebertaForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</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/v4.25.1/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/v4.25.1/src/transformers/models/deberta/modeling_deberta.py#L1376"}}),qa=new te({props:{name:"forward",anchor:"transformers.DebertaForQuestionAnswering.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:"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.DebertaForQuestionAnswering.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/v4.25.1/en/model_doc/deberta#transformers.DebertaTokenizer">DebertaTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.DebertaForQuestionAnswering.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.DebertaForQuestionAnswering.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.DebertaForQuestionAnswering.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.DebertaForQuestionAnswering.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DebertaForQuestionAnswering.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.DebertaForQuestionAnswering.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.DebertaForQuestionAnswering.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/v4.25.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DebertaForQuestionAnswering.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.DebertaForQuestionAnswering.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/v4.25.1/src/transformers/models/deberta/modeling_deberta.py#L1389",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig"
>DebertaConfig</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/v4.25.1/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput"
>transformers.modeling_outputs.QuestionAnsweringModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),yn=new rt({props:{$$slots:{default:[Wv]},$$scope:{ctx:x}}}),$n=new _e({props:{anchor:"transformers.DebertaForQuestionAnswering.forward.example",$$slots:{default:[Hv]},$$scope:{ctx:x}}}),Dn=new _e({props:{anchor:"transformers.DebertaForQuestionAnswering.forward.example-2",$$slots:{default:[Uv]},$$scope:{ctx:x}}}),za=new ot({}),ja=new te({props:{name:"class transformers.TFDebertaModel",anchor:"transformers.TFDebertaModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDebertaModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</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/v4.25.1/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/v4.25.1/src/transformers/models/deberta/modeling_tf_deberta.py#L1095"}}),Fn=new rt({props:{$$slots:{default:[Qv]},$$scope:{ctx:x}}}),La=new te({props:{name:"call",anchor:"transformers.TFDebertaModel.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.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:"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.TFDebertaModel.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaTokenizer">DebertaTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.TFDebertaModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDebertaModel.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFDebertaModel.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFDebertaModel.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDebertaModel.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.`,name:"output_attentions"},{anchor:"transformers.TFDebertaModel.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.`,name:"output_hidden_states"},{anchor:"transformers.TFDebertaModel.call.return_dict",description:"<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;\nWhether or not to return a [`~utils.ModelOutput&#x201C;] instead of a plain tuple.",name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/deberta/modeling_tf_deberta.py#L1101",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutput"
>transformers.modeling_tf_outputs.TFBaseModelOutput</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/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig"
>DebertaConfig</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>hidden_states</strong> (<code>tuple(tf.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>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/v4.25.1/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutput"
>transformers.modeling_tf_outputs.TFBaseModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),xn=new rt({props:{$$slots:{default:[Kv]},$$scope:{ctx:x}}}),qn=new _e({props:{anchor:"transformers.TFDebertaModel.call.example",$$slots:{default:[Gv]},$$scope:{ctx:x}}}),Ba=new ot({}),Ra=new te({props:{name:"class transformers.TFDebertaPreTrainedModel",anchor:"transformers.TFDebertaPreTrainedModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/deberta/modeling_tf_deberta.py#L995"}}),Ia=new te({props:{name:"call",anchor:"transformers.TFDebertaPreTrainedModel.call",parameters:[{name:"inputs",val:""},{name:"training",val:" = None"},{name:"mask",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/keras/engine/training.py#L559",returnDescription:`
<p>A tensor if there is a single output, or
a list of tensors if there are more than one outputs.</p>
`}}),Oa=new ot({}),Na=new te({props:{name:"class transformers.TFDebertaForMaskedLM",anchor:"transformers.TFDebertaForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDebertaForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</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/v4.25.1/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/v4.25.1/src/transformers/models/deberta/modeling_tf_deberta.py#L1143"}}),Mn=new rt({props:{$$slots:{default:[Vv]},$$scope:{ctx:x}}}),Qa=new te({props:{name:"call",anchor:"transformers.TFDebertaForMaskedLM.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.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:"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.TFDebertaForMaskedLM.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaTokenizer">DebertaTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.TFDebertaForMaskedLM.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDebertaForMaskedLM.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFDebertaForMaskedLM.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFDebertaForMaskedLM.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDebertaForMaskedLM.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.`,name:"output_attentions"},{anchor:"transformers.TFDebertaForMaskedLM.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.`,name:"output_hidden_states"},{anchor:"transformers.TFDebertaForMaskedLM.call.return_dict",description:"<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;\nWhether or not to return a [`~utils.ModelOutput&#x201C;] instead of a plain tuple.",name:"return_dict"},{anchor:"transformers.TFDebertaForMaskedLM.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> or <code>np.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are ignored (masked), the
loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/deberta/modeling_tf_deberta.py#L1159",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig"
>DebertaConfig</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/v4.25.1/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput"
>transformers.modeling_tf_outputs.TFMaskedLMOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Cn=new rt({props:{$$slots:{default:[Jv]},$$scope:{ctx:x}}}),Pn=new _e({props:{anchor:"transformers.TFDebertaForMaskedLM.call.example",$$slots:{default:[Xv]},$$scope:{ctx:x}}}),An=new _e({props:{anchor:"transformers.TFDebertaForMaskedLM.call.example-2",$$slots:{default:[Yv]},$$scope:{ctx:x}}}),Ka=new ot({}),Ga=new te({props:{name:"class transformers.TFDebertaForSequenceClassification",anchor:"transformers.TFDebertaForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDebertaForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</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/v4.25.1/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/v4.25.1/src/transformers/models/deberta/modeling_tf_deberta.py#L1226"}}),Bn=new rt({props:{$$slots:{default:[Zv]},$$scope:{ctx:x}}}),Za=new te({props:{name:"call",anchor:"transformers.TFDebertaForSequenceClassification.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.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:"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.TFDebertaForSequenceClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaTokenizer">DebertaTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.TFDebertaForSequenceClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDebertaForSequenceClassification.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFDebertaForSequenceClassification.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFDebertaForSequenceClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDebertaForSequenceClassification.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.`,name:"output_attentions"},{anchor:"transformers.TFDebertaForSequenceClassification.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.`,name:"output_hidden_states"},{anchor:"transformers.TFDebertaForSequenceClassification.call.return_dict",description:"<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;\nWhether or not to return a [`~utils.ModelOutput&#x201C;] instead of a plain tuple.",name:"return_dict"},{anchor:"transformers.TFDebertaForSequenceClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> or <code>np.ndarray</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/deberta/modeling_tf_deberta.py#L1244",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig"
>DebertaConfig</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/v4.25.1/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Rn=new rt({props:{$$slots:{default:[eT]},$$scope:{ctx:x}}}),In=new _e({props:{anchor:"transformers.TFDebertaForSequenceClassification.call.example",$$slots:{default:[tT]},$$scope:{ctx:x}}}),Sn=new _e({props:{anchor:"transformers.TFDebertaForSequenceClassification.call.example-2",$$slots:{default:[oT]},$$scope:{ctx:x}}}),er=new ot({}),tr=new te({props:{name:"class transformers.TFDebertaForTokenClassification",anchor:"transformers.TFDebertaForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDebertaForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</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/v4.25.1/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/v4.25.1/src/transformers/models/deberta/modeling_tf_deberta.py#L1314"}}),Nn=new rt({props:{$$slots:{default:[nT]},$$scope:{ctx:x}}}),rr=new te({props:{name:"call",anchor:"transformers.TFDebertaForTokenClassification.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.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:"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.TFDebertaForTokenClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaTokenizer">DebertaTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.TFDebertaForTokenClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDebertaForTokenClassification.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFDebertaForTokenClassification.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFDebertaForTokenClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDebertaForTokenClassification.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.`,name:"output_attentions"},{anchor:"transformers.TFDebertaForTokenClassification.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.`,name:"output_hidden_states"},{anchor:"transformers.TFDebertaForTokenClassification.call.return_dict",description:"<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;\nWhether or not to return a [`~utils.ModelOutput&#x201C;] instead of a plain tuple.",name:"return_dict"},{anchor:"transformers.TFDebertaForTokenClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> or <code>np.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the token classification loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/deberta/modeling_tf_deberta.py#L1326",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig"
>DebertaConfig</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/v4.25.1/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput"
>transformers.modeling_tf_outputs.TFTokenClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Wn=new rt({props:{$$slots:{default:[sT]},$$scope:{ctx:x}}}),Hn=new _e({props:{anchor:"transformers.TFDebertaForTokenClassification.call.example",$$slots:{default:[aT]},$$scope:{ctx:x}}}),Un=new _e({props:{anchor:"transformers.TFDebertaForTokenClassification.call.example-2",$$slots:{default:[rT]},$$scope:{ctx:x}}}),ir=new ot({}),lr=new te({props:{name:"class transformers.TFDebertaForQuestionAnswering",anchor:"transformers.TFDebertaForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDebertaForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</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/v4.25.1/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/v4.25.1/src/transformers/models/deberta/modeling_tf_deberta.py#L1392"}}),Kn=new rt({props:{$$slots:{default:[iT]},$$scope:{ctx:x}}}),fr=new te({props:{name:"call",anchor:"transformers.TFDebertaForQuestionAnswering.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.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:"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.TFDebertaForQuestionAnswering.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaTokenizer">DebertaTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.TFDebertaForQuestionAnswering.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDebertaForQuestionAnswering.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFDebertaForQuestionAnswering.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFDebertaForQuestionAnswering.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDebertaForQuestionAnswering.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.`,name:"output_attentions"},{anchor:"transformers.TFDebertaForQuestionAnswering.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.`,name:"output_hidden_states"},{anchor:"transformers.TFDebertaForQuestionAnswering.call.return_dict",description:"<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;\nWhether or not to return a [`~utils.ModelOutput&#x201C;] instead of a plain tuple.",name:"return_dict"},{anchor:"transformers.TFDebertaForQuestionAnswering.call.start_positions",description:`<strong>start_positions</strong> (<code>tf.Tensor</code> or <code>np.ndarray</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the 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.TFDebertaForQuestionAnswering.call.end_positions",description:`<strong>end_positions</strong> (<code>tf.Tensor</code> or <code>np.ndarray</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"end_positions"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/deberta/modeling_tf_deberta.py#L1403",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig"
>DebertaConfig</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/v4.25.1/en/main_classes/output#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput"
>transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
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disentangled attention mechanism, where each word is represented using two vectors that encode its content and
position, respectively, and the attention weights among words are computed using disentangled matrices on their
contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to
predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency
of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of
the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9%
(90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and
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BERT model released in 2018 and Facebook\u2019s RoBERTa model released in 2019.`),gr.forEach(t),ve=h(o),Q=r(o,"P",{});var ed=i(Q);ze=s(ed,`It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in
RoBERTa.`),ed.forEach(t),Te=h(o),K=r(o,"P",{});var td=i(K);je=s(td,"The abstract from the paper is the following:"),td.forEach(t),ye=h(o),ne=r(o,"P",{});var od=i(ne);M=r(od,"EM",{});var _r=i(M);C=s(_r,`Recent progress in pre-trained neural language models has significantly improved the performance of many natural
language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with
disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the
disentangled attention mechanism, where each word is represented using two vectors that encode its content and
position, respectively, and the attention weights among words are computed using disentangled matrices on their
contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to
predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency
of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of
the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9%
(90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and
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the model was not pretrained this way, it might yield a decrease in performance.`),fc.forEach(t),wf=h(tt),T(tn.$$.fragment,tt),vf=h(tt),Hs=r(tt,"P",{});var mc=i(Hs);Tf=s(mc,"This tokenizer inherits from "),Wr=r(mc,"A",{href:!0});var Dk=i(Wr);yf=s(Dk,"PreTrainedTokenizerFast"),Dk.forEach(t),$f=s(mc,` which contains most of the main methods. Users should
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adding special tokens. A DeBERTa sequence has the following format:`),Ek.forEach(t),xf=h(ri),Qs=r(ri,"UL",{});var uc=i(Qs);Hi=r(uc,"LI",{});var Fk=i(Hi);qf=s(Fk,"single sequence: [CLS] X [SEP]"),Fk.forEach(t),zf=h(uc),Ui=r(uc,"LI",{});var xk=i(Ui);jf=s(xk,"pair of sequences: [CLS] A [SEP] B [SEP]"),xk.forEach(t),uc.forEach(t),ri.forEach(t),Mf=h(tt),Et=r(tt,"DIV",{class:!0});var es=i(Et);T(Ks.$$.fragment,es),Cf=h(es),Qi=r(es,"P",{});var qk=i(Qi);Pf=s(qk,"Create a mask from the two sequences passed to be used in a sequence-pair classification task. A DeBERTa"),qk.forEach(t),Af=h(es),T(on.$$.fragment,es),Lf=h(es),bo=r(es,"P",{});var ii=i(bo);Bf=s(ii,"If "),Ki=r(ii,"CODE",{});var zk=i(Ki);Rf=s(zk,"token_ids_1"),zk.forEach(t),If=s(ii," is "),Gi=r(ii,"CODE",{});var jk=i(Gi);Sf=s(jk,"None"),jk.forEach(t),Of=s(ii,", this method only returns the first portion of the mask (0s)."),ii.forEach(t),es.forEach(t),tt.forEach(t),Pd=h(o),ko=r(o,"H2",{class:!0});var gc=i(ko);nn=r(gc,"A",{id:!0,class:!0,href:!0});var Mk=i(nn);Vi=r(Mk,"SPAN",{});var Ck=i(Vi);T(Gs.$$.fragment,Ck),Ck.forEach(t),Mk.forEach(t),Nf=h(gc),Ji=r(gc,"SPAN",{});var Pk=i(Ji);Wf=s(Pk,"DebertaModel"),Pk.forEach(t),gc.forEach(t),Ad=h(o),vt=r(o,"DIV",{class:!0});var ts=i(vt);T(Vs.$$.fragment,ts),Hf=h(ts),Js=r(ts,"P",{});var _c=i(Js);Uf=s(_c,`The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.
The DeBERTa model was proposed in `),Xs=r(_c,"A",{href:!0,rel:!0});var Ak=i(Xs);Qf=s(Ak,`DeBERTa: Decoding-enhanced BERT with Disentangled
Attention`),Ak.forEach(t),Kf=s(_c,` by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It\u2019s build
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.`),_c.forEach(t),Gf=h(ts),Ys=r(ts,"P",{});var bc=i(Ys);Vf=s(bc,"This model is also a PyTorch "),Zs=r(bc,"A",{href:!0,rel:!0});var Lk=i(Zs);Jf=s(Lk,"torch.nn.Module"),Lk.forEach(t),Xf=s(bc,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),bc.forEach(t),Yf=h(ts),Ft=r(ts,"DIV",{class:!0});var os=i(Ft);T(ea.$$.fragment,os),Zf=h(os),wo=r(os,"P",{});var li=i(wo);em=s(li,"The "),Hr=r(li,"A",{href:!0});var Bk=i(Hr);tm=s(Bk,"DebertaModel"),Bk.forEach(t),om=s(li," forward method, overrides the "),Xi=r(li,"CODE",{});var Rk=i(Xi);nm=s(Rk,"__call__"),Rk.forEach(t),sm=s(li," special method."),li.forEach(t),am=h(os),T(sn.$$.fragment,os),rm=h(os),T(an.$$.fragment,os),os.forEach(t),ts.forEach(t),Ld=h(o),vo=r(o,"H2",{class:!0});var kc=i(vo);rn=r(kc,"A",{id:!0,class:!0,href:!0});var Ik=i(rn);Yi=r(Ik,"SPAN",{});var Sk=i(Yi);T(ta.$$.fragment,Sk),Sk.forEach(t),Ik.forEach(t),im=h(kc),Zi=r(kc,"SPAN",{});var Ok=i(Zi);lm=s(Ok,"DebertaPreTrainedModel"),Ok.forEach(t),kc.forEach(t),Bd=h(o),To=r(o,"DIV",{class:!0});var wc=i(To);T(oa.$$.fragment,wc),dm=h(wc),el=r(wc,"P",{});var Nk=i(el);cm=s(Nk,`An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.`),Nk.forEach(t),wc.forEach(t),Rd=h(o),yo=r(o,"H2",{class:!0});var vc=i(yo);ln=r(vc,"A",{id:!0,class:!0,href:!0});var Wk=i(ln);tl=r(Wk,"SPAN",{});var Hk=i(tl);T(na.$$.fragment,Hk),Hk.forEach(t),Wk.forEach(t),pm=h(vc),ol=r(vc,"SPAN",{});var Uk=i(ol);hm=s(Uk,"DebertaForMaskedLM"),Uk.forEach(t),vc.forEach(t),Id=h(o),Tt=r(o,"DIV",{class:!0});var ns=i(Tt);T(sa.$$.fragment,ns),fm=h(ns),$o=r(ns,"P",{});var di=i($o);mm=s(di,"DeBERTa Model with a "),nl=r(di,"CODE",{});var Qk=i(nl);um=s(Qk,"language modeling"),Qk.forEach(t),gm=s(di,` head on top.
The DeBERTa model was proposed in `),aa=r(di,"A",{href:!0,rel:!0});var Kk=i(aa);_m=s(Kk,`DeBERTa: Decoding-enhanced BERT with Disentangled
Attention`),Kk.forEach(t),bm=s(di,` by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It\u2019s build
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.`),di.forEach(t),km=h(ns),ra=r(ns,"P",{});var Tc=i(ra);wm=s(Tc,"This model is also a PyTorch "),ia=r(Tc,"A",{href:!0,rel:!0});var Gk=i(ia);vm=s(Gk,"torch.nn.Module"),Gk.forEach(t),Tm=s(Tc,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),Tc.forEach(t),ym=h(ns),ht=r(ns,"DIV",{class:!0});var Yt=i(ht);T(la.$$.fragment,Yt),$m=h(Yt),Do=r(Yt,"P",{});var ci=i(Do);Dm=s(ci,"The "),Ur=r(ci,"A",{href:!0});var Vk=i(Ur);Em=s(Vk,"DebertaForMaskedLM"),Vk.forEach(t),Fm=s(ci," forward method, overrides the "),sl=r(ci,"CODE",{});var Jk=i(sl);xm=s(Jk,"__call__"),Jk.forEach(t),qm=s(ci," special method."),ci.forEach(t),zm=h(Yt),T(dn.$$.fragment,Yt),jm=h(Yt),T(cn.$$.fragment,Yt),Mm=h(Yt),T(pn.$$.fragment,Yt),Yt.forEach(t),ns.forEach(t),Sd=h(o),Eo=r(o,"H2",{class:!0});var yc=i(Eo);hn=r(yc,"A",{id:!0,class:!0,href:!0});var Xk=i(hn);al=r(Xk,"SPAN",{});var Yk=i(al);T(da.$$.fragment,Yk),Yk.forEach(t),Xk.forEach(t),Cm=h(yc),rl=r(yc,"SPAN",{});var Zk=i(rl);Pm=s(Zk,"DebertaForSequenceClassification"),Zk.forEach(t),yc.forEach(t),Od=h(o),it=r(o,"DIV",{class:!0});var Zt=i(it);T(ca.$$.fragment,Zt),Am=h(Zt),il=r(Zt,"P",{});var ew=i(il);Lm=s(ew,`DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`),ew.forEach(t),Bm=h(Zt),pa=r(Zt,"P",{});var $c=i(pa);Rm=s($c,"The DeBERTa model was proposed in "),ha=r($c,"A",{href:!0,rel:!0});var tw=i(ha);Im=s(tw,`DeBERTa: Decoding-enhanced BERT with Disentangled
Attention`),tw.forEach(t),Sm=s($c,` by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It\u2019s build
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.`),$c.forEach(t),Om=h(Zt),fa=r(Zt,"P",{});var Dc=i(fa);Nm=s(Dc,"This model is also a PyTorch "),ma=r(Dc,"A",{href:!0,rel:!0});var ow=i(ma);Wm=s(ow,"torch.nn.Module"),ow.forEach(t),Hm=s(Dc,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),Dc.forEach(t),Um=h(Zt),et=r(Zt,"DIV",{class:!0});var kt=i(et);T(ua.$$.fragment,kt),Qm=h(kt),Fo=r(kt,"P",{});var pi=i(Fo);Km=s(pi,"The "),Qr=r(pi,"A",{href:!0});var nw=i(Qr);Gm=s(nw,"DebertaForSequenceClassification"),nw.forEach(t),Vm=s(pi," forward method, overrides the "),ll=r(pi,"CODE",{});var sw=i(ll);Jm=s(sw,"__call__"),sw.forEach(t),Xm=s(pi," special method."),pi.forEach(t),Ym=h(kt),T(fn.$$.fragment,kt),Zm=h(kt),T(mn.$$.fragment,kt),eu=h(kt),T(un.$$.fragment,kt),tu=h(kt),T(gn.$$.fragment,kt),ou=h(kt),T(_n.$$.fragment,kt),kt.forEach(t),Zt.forEach(t),Nd=h(o),xo=r(o,"H2",{class:!0});var Ec=i(xo);bn=r(Ec,"A",{id:!0,class:!0,href:!0});var aw=i(bn);dl=r(aw,"SPAN",{});var rw=i(dl);T(ga.$$.fragment,rw),rw.forEach(t),aw.forEach(t),nu=h(Ec),cl=r(Ec,"SPAN",{});var iw=i(cl);su=s(iw,"DebertaForTokenClassification"),iw.forEach(t),Ec.forEach(t),Wd=h(o),lt=r(o,"DIV",{class:!0});var eo=i(lt);T(_a.$$.fragment,eo),au=h(eo),pl=r(eo,"P",{});var lw=i(pl);ru=s(lw,`DeBERTa 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.`),lw.forEach(t),iu=h(eo),ba=r(eo,"P",{});var Fc=i(ba);lu=s(Fc,"The DeBERTa model was proposed in "),ka=r(Fc,"A",{href:!0,rel:!0});var dw=i(ka);du=s(dw,`DeBERTa: Decoding-enhanced BERT with Disentangled
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on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.`),Fc.forEach(t),pu=h(eo),wa=r(eo,"P",{});var xc=i(wa);hu=s(xc,"This model is also a PyTorch "),va=r(xc,"A",{href:!0,rel:!0});var cw=i(va);fu=s(cw,"torch.nn.Module"),cw.forEach(t),mu=s(xc,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),xc.forEach(t),uu=h(eo),ft=r(eo,"DIV",{class:!0});var to=i(ft);T(Ta.$$.fragment,to),gu=h(to),qo=r(to,"P",{});var hi=i(qo);_u=s(hi,"The "),Kr=r(hi,"A",{href:!0});var pw=i(Kr);bu=s(pw,"DebertaForTokenClassification"),pw.forEach(t),ku=s(hi," forward method, overrides the "),hl=r(hi,"CODE",{});var hw=i(hl);wu=s(hw,"__call__"),hw.forEach(t),vu=s(hi," special method."),hi.forEach(t),Tu=h(to),T(kn.$$.fragment,to),yu=h(to),T(wn.$$.fragment,to),$u=h(to),T(vn.$$.fragment,to),to.forEach(t),eo.forEach(t),Hd=h(o),zo=r(o,"H2",{class:!0});var qc=i(zo);Tn=r(qc,"A",{id:!0,class:!0,href:!0});var fw=i(Tn);fl=r(fw,"SPAN",{});var mw=i(fl);T(ya.$$.fragment,mw),mw.forEach(t),fw.forEach(t),Du=h(qc),ml=r(qc,"SPAN",{});var uw=i(ml);Eu=s(uw,"DebertaForQuestionAnswering"),uw.forEach(t),qc.forEach(t),Ud=h(o),dt=r(o,"DIV",{class:!0});var oo=i(dt);T($a.$$.fragment,oo),Fu=h(oo),jo=r(oo,"P",{});var fi=i(jo);xu=s(fi,`DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
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Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
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The DeBERTa model was proposed in `),Ca=r(Cc,"A",{href:!0,rel:!0});var Dw=i(Ca);tg=s(Dw,`DeBERTa: Decoding-enhanced BERT with Disentangled
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improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.`),Cc.forEach(t),ng=h(so),Pa=r(so,"P",{});var Pc=i(Pa);sg=s(Pc,"This model is also a "),Aa=r(Pc,"A",{href:!0,rel:!0});var Ew=i(Aa);ag=s(Ew,"tf.keras.Model"),Ew.forEach(t),rg=s(Pc,` 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.`),Pc.forEach(t),ig=h(so),T(Fn.$$.fragment,so),lg=h(so),xt=r(so,"DIV",{class:!0});var ss=i(xt);T(La.$$.fragment,ss),dg=h(ss),Po=r(ss,"P",{});var ui=i(Po);cg=s(ui,"The "),Vr=r(ui,"A",{href:!0});var Fw=i(Vr);pg=s(Fw,"TFDebertaModel"),Fw.forEach(t),hg=s(ui," forward method, overrides the "),wl=r(ui,"CODE",{});var xw=i(wl);fg=s(xw,"__call__"),xw.forEach(t),mg=s(ui," special method."),ui.forEach(t),ug=h(ss),T(xn.$$.fragment,ss),gg=h(ss),T(qn.$$.fragment,ss),ss.forEach(t),so.forEach(t),Gd=h(o),Ao=r(o,"H2",{class:!0});var Ac=i(Ao);zn=r(Ac,"A",{id:!0,class:!0,href:!0});var qw=i(zn);vl=r(qw,"SPAN",{});var zw=i(vl);T(Ba.$$.fragment,zw),zw.forEach(t),qw.forEach(t),_g=h(Ac),Tl=r(Ac,"SPAN",{});var jw=i(Tl);bg=s(jw,"TFDebertaPreTrainedModel"),jw.forEach(t),Ac.forEach(t),Vd=h(o),Pt=r(o,"DIV",{class:!0});var gi=i(Pt);T(Ra.$$.fragment,gi),kg=h(gi),yl=r(gi,"P",{});var Mw=i(yl);wg=s(Mw,`An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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overridden when subclassing `),El=r(ao,"CODE",{});var Aw=i(El);zg=s(Aw,"tf.keras.Model"),Aw.forEach(t),jg=s(ao,`.
To call a model on an input, always use the `),Fl=r(ao,"CODE",{});var Lw=i(Fl);Mg=s(Lw,"__call__()"),Lw.forEach(t),Cg=s(ao,` method,
i.e. `),xl=r(ao,"CODE",{});var Bw=i(xl);Pg=s(Bw,"model(inputs)"),Bw.forEach(t),Ag=s(ao,", which relies on the underlying "),ql=r(ao,"CODE",{});var Rw=i(ql);Lg=s(Rw,"call()"),Rw.forEach(t),Bg=s(ao," method."),ao.forEach(t),as.forEach(t),gi.forEach(t),Jd=h(o),Lo=r(o,"H2",{class:!0});var Bc=i(Lo);jn=r(Bc,"A",{id:!0,class:!0,href:!0});var Iw=i(jn);zl=r(Iw,"SPAN",{});var Sw=i(zl);T(Oa.$$.fragment,Sw),Sw.forEach(t),Iw.forEach(t),Rg=h(Bc),jl=r(Bc,"SPAN",{});var Ow=i(jl);Ig=s(Ow,"TFDebertaForMaskedLM"),Ow.forEach(t),Bc.forEach(t),Xd=h(o),pt=r(o,"DIV",{class:!0});var ro=i(pt);T(Na.$$.fragment,ro),Sg=h(ro),Bo=r(ro,"P",{});var _i=i(Bo);Og=s(_i,"DeBERTa Model with a "),Ml=r(_i,"CODE",{});var Nw=i(Ml);Ng=s(Nw,"language modeling"),Nw.forEach(t),Wg=s(_i,` head on top.
The DeBERTa model was proposed in `),Wa=r(_i,"A",{href:!0,rel:!0});var Ww=i(Wa);Hg=s(Ww,`DeBERTa: Decoding-enhanced BERT with Disentangled
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on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.`),_i.forEach(t),Qg=h(ro),Ha=r(ro,"P",{});var Rc=i(Ha);Kg=s(Rc,"This model is also a "),Ua=r(Rc,"A",{href:!0,rel:!0});var Hw=i(Ua);Gg=s(Hw,"tf.keras.Model"),Hw.forEach(t),Vg=s(Rc,` 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.`),Rc.forEach(t),Jg=h(ro),T(Mn.$$.fragment,ro),Xg=h(ro),ut=r(ro,"DIV",{class:!0});var io=i(ut);T(Qa.$$.fragment,io),Yg=h(io),Ro=r(io,"P",{});var bi=i(Ro);Zg=s(bi,"The "),Jr=r(bi,"A",{href:!0});var Uw=i(Jr);e_=s(Uw,"TFDebertaForMaskedLM"),Uw.forEach(t),t_=s(bi," forward method, overrides the "),Cl=r(bi,"CODE",{});var Qw=i(Cl);o_=s(Qw,"__call__"),Qw.forEach(t),n_=s(bi," special method."),bi.forEach(t),s_=h(io),T(Cn.$$.fragment,io),a_=h(io),T(Pn.$$.fragment,io),r_=h(io),T(An.$$.fragment,io),io.forEach(t),ro.forEach(t),Yd=h(o),Io=r(o,"H2",{class:!0});var Ic=i(Io);Ln=r(Ic,"A",{id:!0,class:!0,href:!0});var Kw=i(Ln);Pl=r(Kw,"SPAN",{});var Gw=i(Pl);T(Ka.$$.fragment,Gw),Gw.forEach(t),Kw.forEach(t),i_=h(Ic),Al=r(Ic,"SPAN",{});var Vw=i(Al);l_=s(Vw,"TFDebertaForSequenceClassification"),Vw.forEach(t),Ic.forEach(t),Zd=h(o),nt=r(o,"DIV",{class:!0});var zt=i(nt);T(Ga.$$.fragment,zt),d_=h(zt),Ll=r(zt,"P",{});var Jw=i(Ll);c_=s(Jw,`DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
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