import{S as lT,i as dT,s as cT,e as a,k as c,w as T,t as n,M as pT,c as r,d as t,m as p,a as i,x as y,h as s,b as f,G as e,g,y as v,q as $,o as D,B as E,v as hT,L as Ue}from"../../chunks/vendor-hf-doc-builder.js";import{T as at}from"../../chunks/Tip-hf-doc-builder.js";import{D as te}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Qe}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as tt}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as He}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";import{P as Sc}from"../../chunks/PipelineTag-hf-doc-builder.js";function fT(x){let d,b,m,u,k;return u=new Qe({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"),b=n("Example:"),m=c(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var h=i(d);b=s(h,"Example:"),h.forEach(t),m=p(l),y(u.$$.fragment,l)},m(l,h){g(l,d,h),e(d,b),g(l,m,h),v(u,l,h),k=!0},p:Ue,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function mT(x){let d,b,m,u,k;return u=new Qe({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"),b=n("be encoded differently whether it is at the beginning of the sentence (without space) or not:"),m=c(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var h=i(d);b=s(h,"be encoded differently whether it is at the beginning of the sentence (without space) or not:"),h.forEach(t),m=p(l),y(u.$$.fragment,l)},m(l,h){g(l,d,h),e(d,b),g(l,m,h),v(u,l,h),k=!0},p:Ue,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function uT(x){let d,b,m,u,k;return{c(){d=a("p"),b=n("When used with "),m=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 h=i(d);b=s(h,"When used with "),m=r(h,"CODE",{});var z=i(m);u=s(z,"is_split_into_words=True"),z.forEach(t),k=s(h,", this tokenizer will add a space before each word (even the first one)."),h.forEach(t)},m(l,h){g(l,d,h),e(d,b),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function gT(x){let d,b,m,u,k;return u=new Qe({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"),b=n("sequence pair mask has the following format:"),m=c(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var h=i(d);b=s(h,"sequence pair mask has the following format:"),h.forEach(t),m=p(l),y(u.$$.fragment,l)},m(l,h){g(l,d,h),e(d,b),g(l,m,h),v(u,l,h),k=!0},p:Ue,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function _T(x){let d,b,m,u,k;return u=new Qe({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"),b=n("be encoded differently whether it is at the beginning of the sentence (without space) or not:"),m=c(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var h=i(d);b=s(h,"be encoded differently whether it is at the beginning of the sentence (without space) or not:"),h.forEach(t),m=p(l),y(u.$$.fragment,l)},m(l,h){g(l,d,h),e(d,b),g(l,m,h),v(u,l,h),k=!0},p:Ue,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function bT(x){let d,b,m,u,k,l,h,z;return{c(){d=a("p"),b=n("When used with "),m=a("code"),u=n("is_split_into_words=True"),k=n(", this tokenizer needs to be instantiated with "),l=a("code"),h=n("add_prefix_space=True"),z=n(".")},l(ae){d=r(ae,"P",{});var V=i(d);b=s(V,"When used with "),m=r(V,"CODE",{});var B=i(m);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);h=s(oe,"add_prefix_space=True"),oe.forEach(t),z=s(V,"."),V.forEach(t)},m(ae,V){g(ae,d,V),e(d,b),e(d,m),e(m,u),e(d,k),e(d,l),e(l,h),e(d,z)},d(ae){ae&&t(d)}}}function kT(x){let d,b,m,u,k;return u=new Qe({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"),b=n("sequence pair mask has the following format:"),m=c(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var h=i(d);b=s(h,"sequence pair mask has the following format:"),h.forEach(t),m=p(l),y(u.$$.fragment,l)},m(l,h){g(l,d,h),e(d,b),g(l,m,h),v(u,l,h),k=!0},p:Ue,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function wT(x){let d,b,m,u,k;return{c(){d=a("p"),b=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=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 h=i(d);b=s(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r(h,"CODE",{});var z=i(m);u=s(z,"Module"),z.forEach(t),k=s(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(l,h){g(l,d,h),e(d,b),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function TT(x){let d,b,m,u,k;return u=new Qe({props:{code:`from transformers import AutoTokenizer, DebertaModel
import torch

tokenizer = AutoTokenizer.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> AutoTokenizer, 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 = AutoTokenizer.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"),b=n("Example:"),m=c(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var h=i(d);b=s(h,"Example:"),h.forEach(t),m=p(l),y(u.$$.fragment,l)},m(l,h){g(l,d,h),e(d,b),g(l,m,h),v(u,l,h),k=!0},p:Ue,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function yT(x){let d,b,m,u,k;return{c(){d=a("p"),b=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=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 h=i(d);b=s(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r(h,"CODE",{});var z=i(m);u=s(z,"Module"),z.forEach(t),k=s(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(l,h){g(l,d,h),e(d,b),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function vT(x){let d,b,m,u,k;return u=new Qe({props:{code:`from transformers import AutoTokenizer, DebertaForMaskedLM
import torch

tokenizer = AutoTokenizer.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)

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

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

<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(){d=a("p"),b=n("Example:"),m=c(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var h=i(d);b=s(h,"Example:"),h.forEach(t),m=p(l),y(u.$$.fragment,l)},m(l,h){g(l,d,h),e(d,b),g(l,m,h),v(u,l,h),k=!0},p:Ue,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function $T(x){let d,b,m,u,k;return{c(){d=a("p"),b=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=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 h=i(d);b=s(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r(h,"CODE",{});var z=i(m);u=s(z,"Module"),z.forEach(t),k=s(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(l,h){g(l,d,h),e(d,b),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function DT(x){let d,b,m,u,k;return u=new Qe({props:{code:`import torch
from transformers import AutoTokenizer, DebertaForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-base")
model = DebertaForSequenceClassification.from_pretrained("microsoft/deberta-base")

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

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

predicted_class_id = logits.argmax().item()

# To train a model on \`num_labels\` classes, you can pass \`num_labels=num_labels\` to \`.from_pretrained(...)\`
num_labels = len(model.config.id2label)
model = DebertaForSequenceClassification.from_pretrained("microsoft/deberta-base", num_labels=num_labels)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/deberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DebertaForSequenceClassification.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><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = torch.tensor([<span class="hljs-number">1</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss`}}),{c(){d=a("p"),b=n("Example of single-label classification:"),m=c(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var h=i(d);b=s(h,"Example of single-label classification:"),h.forEach(t),m=p(l),y(u.$$.fragment,l)},m(l,h){g(l,d,h),e(d,b),g(l,m,h),v(u,l,h),k=!0},p:Ue,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function ET(x){let d,b,m,u,k;return u=new Qe({props:{code:`import torch
from transformers import AutoTokenizer, DebertaForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-base")
model = DebertaForSequenceClassification.from_pretrained("microsoft/deberta-base", problem_type="multi_label_classification")

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

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

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

# To train a model on \`num_labels\` classes, you can pass \`num_labels=num_labels\` to \`.from_pretrained(...)\`
num_labels = len(model.config.id2label)
model = DebertaForSequenceClassification.from_pretrained(
    "microsoft/deberta-base", num_labels=num_labels, problem_type="multi_label_classification"
)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/deberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DebertaForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;microsoft/deberta-base&quot;</span>, problem_type=<span class="hljs-string">&quot;multi_label_classification&quot;</span>)

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

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

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

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

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

tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-base")
model = DebertaForTokenClassification.from_pretrained("microsoft/deberta-base")

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

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

predicted_token_class_ids = logits.argmax(-1)

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

labels = predicted_token_class_ids
loss = model(**inputs, labels=labels).loss`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, 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 = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/deberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DebertaForTokenClassification.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-meta">... </span>    <span class="hljs-string">&quot;HuggingFace is a company based in Paris and New York&quot;</span>, add_special_tokens=<span class="hljs-literal">False</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>
<span class="hljs-meta">... </span>)

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

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

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

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

tokenizer = AutoTokenizer.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)

# 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-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, 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 = AutoTokenizer.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>

<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(){d=a("p"),b=n("Example:"),m=c(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var h=i(d);b=s(h,"Example:"),h.forEach(t),m=p(l),y(u.$$.fragment,l)},m(l,h){g(l,d,h),e(d,b),g(l,m,h),v(u,l,h),k=!0},p:Ue,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function jT(x){let d,b,m,u,k,l,h,z,ae,V,B,oe,se,F,ve,G,$e,_e,S,De,le,J,Ee,be,Q,Fe,ke,K,qe,we,ne,M,C,de,X,xe,Te,L,ze,ce,A,je,W,Me,Ce,H,Ae,Pe,I,re,O,me,Z,P,ue,R,ye,fe;return{c(){d=a("p"),b=n("TensorFlow models and layers in "),m=a("code"),u=n("transformers"),k=n(" accept two formats as input:"),l=c(),h=a("ul"),z=a("li"),ae=n("having all inputs as keyword arguments (like PyTorch models), or"),V=c(),B=a("li"),oe=n("having all inputs as a list, tuple or dict in the first positional argument."),se=c(),F=a("p"),ve=n(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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`),R=a("a"),ye=n("subclassing"),fe=n(` then you don\u2019t need to worry
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`),R=r(ee,"A",{href:!0,rel:!0});var Ze=i(R);ye=s(Ze,"subclassing"),Ze.forEach(t),fe=s(ee,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),ee.forEach(t),this.h()},h(){f(R,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),f(R,"rel","nofollow")},m(w,q){g(w,d,q),e(d,b),e(d,m),e(m,u),e(d,k),g(w,l,q),g(w,h,q),e(h,z),e(z,ae),e(h,V),e(h,B),e(B,oe),g(w,se,q),g(w,F,q),e(F,ve),e(F,G),e(G,$e),e(F,_e),e(F,S),e(S,De),e(F,le),e(F,J),e(J,Ee),e(F,be),e(F,Q),e(Q,Fe),e(F,ke),e(F,K),e(K,qe),e(F,we),g(w,ne,q),g(w,M,q),e(M,C),e(C,de),e(C,X),e(X,xe),e(C,Te),e(C,L),e(L,ze),e(M,ce),e(M,A),e(A,je),e(A,W),e(W,Me),e(A,Ce),e(A,H),e(H,Ae),e(M,Pe),e(M,I),e(I,re),e(I,O),e(O,me),g(w,Z,q),g(w,P,q),e(P,ue),e(P,R),e(R,ye),e(P,fe)},d(w){w&&t(d),w&&t(l),w&&t(h),w&&t(se),w&&t(F),w&&t(ne),w&&t(M),w&&t(Z),w&&t(P)}}}function MT(x){let d,b,m,u,k;return{c(){d=a("p"),b=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=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 h=i(d);b=s(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r(h,"CODE",{});var z=i(m);u=s(z,"Module"),z.forEach(t),k=s(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(l,h){g(l,d,h),e(d,b),e(d,m),e(m,u),e(d,k)},d(l){l&&t(d)}}}function CT(x){let d,b,m,u,k;return u=new Qe({props:{code:`from transformers import AutoTokenizer, TFDebertaModel
import tensorflow as tf

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

tokenizer = AutoTokenizer.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)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, 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 = AutoTokenizer.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>)`}}),{c(){d=a("p"),b=n("Example:"),m=c(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var h=i(d);b=s(h,"Example:"),h.forEach(t),m=p(l),y(u.$$.fragment,l)},m(l,h){g(l,d,h),e(d,b),g(l,m,h),v(u,l,h),k=!0},p:Ue,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function BT(x){let d,b;return d=new Qe({props:{code:`labels = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
# mask labels of non-[MASK] tokens
labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)

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

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

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

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

tokenizer = AutoTokenizer.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]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, 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 = AutoTokenizer.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>]`}}),{c(){d=a("p"),b=n("Example:"),m=c(),T(u.$$.fragment)},l(l){d=r(l,"P",{});var h=i(d);b=s(h,"Example:"),h.forEach(t),m=p(l),y(u.$$.fragment,l)},m(l,h){g(l,d,h),e(d,b),g(l,m,h),v(u,l,h),k=!0},p:Ue,i(l){k||($(u.$$.fragment,l),k=!0)},o(l){D(u.$$.fragment,l),k=!1},d(l){l&&t(d),l&&t(m),E(u,l)}}}function VT(x){let d,b;return d=new Qe({props:{code:`# target is "nice puppet"
target_start_index = tf.constant([14])
target_end_index = tf.constant([15])

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = tf.math.reduce_mean(outputs.loss)`}}),{c(){T(d.$$.fragment)},l(m){y(d.$$.fragment,m)},m(m,u){v(d,m,u),b=!0},p:Ue,i(m){b||($(d.$$.fragment,m),b=!0)},o(m){D(d.$$.fragment,m),b=!1},d(m){E(d,m)}}}function JT(x){let d,b,m,u,k,l,h,z,ae,V,B,oe,se,F,ve,G,$e,_e,S,De,le,J,Ee,be,Q,Fe,ke,K,qe,we,ne,M,C,de,X,xe,Te,L,ze,ce,A,je,W,Me,Ce,H,Ae,Pe,I,re,O,me,Z,P,ue,R,ye,fe,w,q,Le,ie,pe,Ie,j,Se,Ke,Ge,Ve,Oe,N,U,Je,Xe,Y,ge,We,Re,Ye,ee,Ze,Oc,es,Nc,Wc,Hc,Rt,Tr,Uc,Qc,ts,Kc,Gc,os,Vc,Jc,md,ns,ud,wt,Bt,yr,Xc,Yc,ss,Zc,ep,as,tp,op,np,Lt,vr,sp,ap,rs,rp,ip,is,lp,dp,cp,$r,ls,pp,hp,fp,Dr,ds,mp,up,gd,cs,_d,It,St,Er,gp,_p,ps,bp,kp,hs,wp,Tp,yp,Ot,Fr,vp,$p,fs,Dp,Ep,ms,Fp,qp,xp,qr,us,zp,jp,bd,gs,kd,Nt,Wt,xr,Mp,Cp,_s,Ap,Pp,bs,Rp,Bp,Lp,Ht,zr,Ip,Sp,ks,Op,Np,ws,Wp,Hp,Up,jr,Ts,Qp,Kp,wd,ro,Oo,bi,ys,Gp,ki,Vp,Td,gt,vs,Jp,Mt,Xp,Mr,Yp,Zp,Cr,eh,th,$s,oh,nh,sh,io,ah,Ar,rh,ih,Pr,lh,dh,ch,No,yd,lo,Wo,wi,Ds,ph,Ti,hh,vd,he,Es,fh,yi,mh,uh,vi,gh,_h,Ho,bh,Fs,kh,$i,wh,Th,yh,Uo,vh,qs,$h,Rr,Dh,Eh,Fh,Ut,xs,qh,Di,xh,zh,zs,Ei,jh,Mh,Fi,Ch,Ah,Qo,js,Ph,co,Rh,qi,Bh,Lh,xi,Ih,Sh,Oh,Tt,Ms,Nh,zi,Wh,Hh,Ko,Uh,po,Qh,ji,Kh,Gh,Mi,Vh,Jh,Xh,Br,Cs,$d,ho,Go,Ci,As,Yh,Ai,Zh,Dd,Be,Ps,ef,Rs,tf,Pi,of,nf,sf,Ri,af,rf,Vo,lf,Bs,df,Bi,cf,pf,hf,Jo,ff,Ls,mf,Lr,uf,gf,_f,Qt,Is,bf,Li,kf,wf,Ss,Ii,Tf,yf,Si,vf,$f,yt,Os,Df,Oi,Ef,Ff,Xo,qf,fo,xf,Ni,zf,jf,Wi,Mf,Cf,Ed,mo,Yo,Hi,Ns,Af,Ui,Pf,Fd,_t,Ws,Rf,Hs,Bf,Us,Lf,If,Sf,Qs,Of,Ks,Nf,Wf,Hf,vt,Gs,Uf,uo,Qf,Ir,Kf,Gf,Qi,Vf,Jf,Xf,Zo,Yf,en,qd,go,tn,Ki,Vs,Zf,Gi,em,xd,_o,Js,tm,Vi,om,zd,bo,on,Ji,Xs,nm,Xi,sm,jd,bt,Ys,am,ko,rm,Yi,im,lm,Zs,dm,cm,pm,ea,hm,ta,fm,mm,um,$t,oa,gm,wo,_m,Sr,bm,km,Zi,wm,Tm,ym,nn,vm,sn,Md,To,an,el,na,$m,tl,Dm,Cd,rt,sa,Em,ol,Fm,qm,aa,xm,ra,zm,jm,Mm,ia,Cm,la,Am,Pm,Rm,pt,da,Bm,yo,Lm,Or,Im,Sm,nl,Om,Nm,Wm,rn,Hm,ln,Um,dn,Ad,vo,cn,sl,ca,Qm,al,Km,Pd,it,pa,Gm,rl,Vm,Jm,ha,Xm,fa,Ym,Zm,eu,ma,tu,ua,ou,nu,su,Dt,ga,au,$o,ru,Nr,iu,lu,il,du,cu,pu,pn,hu,hn,Rd,Do,fn,ll,_a,fu,dl,mu,Bd,lt,ba,uu,Eo,gu,cl,_u,bu,pl,ku,wu,Tu,ka,yu,wa,vu,$u,Du,Ta,Eu,ya,Fu,qu,xu,Et,va,zu,Fo,ju,Wr,Mu,Cu,hl,Au,Pu,Ru,mn,Bu,un,Ld,qo,gn,fl,$a,Lu,ml,Iu,Id,dt,Da,Su,Ea,Ou,Fa,Nu,Wu,Hu,qa,Uu,xa,Qu,Ku,Gu,_n,Vu,Ft,za,Ju,xo,Xu,Hr,Yu,Zu,ul,eg,tg,og,bn,ng,kn,Sd,zo,wn,gl,ja,sg,_l,ag,Od,Ct,Ma,rg,bl,ig,lg,qt,Ca,dg,kl,cg,pg,Aa,hg,wl,fg,mg,ug,kt,gg,Tl,_g,bg,yl,kg,wg,vl,Tg,yg,$l,vg,$g,Nd,jo,Tn,Dl,Pa,Dg,El,Eg,Wd,ct,Ra,Fg,Mo,qg,Fl,xg,zg,Ba,jg,Mg,Cg,La,Ag,Ia,Pg,Rg,Bg,yn,Lg,ht,Sa,Ig,Co,Sg,Ur,Og,Ng,ql,Wg,Hg,Ug,vn,Qg,$n,Kg,Dn,Hd,Ao,En,xl,Oa,Gg,zl,Vg,Ud,ot,Na,Jg,jl,Xg,Yg,Wa,Zg,Ha,e_,t_,o_,Ua,n_,Qa,s_,a_,r_,Fn,i_,ft,Ka,l_,Po,d_,Qr,c_,p_,Ml,h_,f_,m_,qn,u_,xn,g_,zn,Qd,Ro,jn,Cl,Ga,__,Al,b_,Kd,nt,Va,k_,Pl,w_,T_,Ja,y_,Xa,v_,$_,D_,Ya,E_,Za,F_,q_,x_,Mn,z_,mt,er,j_,Bo,M_,Kr,C_,A_,Rl,P_,R_,B_,Cn,L_,An,I_,Pn,Gd,Lo,Rn,Bl,tr,S_,Ll,O_,Vd,st,or,N_,Io,W_,Il,H_,U_,Sl,Q_,K_,G_,nr,V_,sr,J_,X_,Y_,ar,Z_,rr,eb,tb,ob,Bn,nb,ut,ir,sb,So,ab,Gr,rb,ib,Ol,lb,db,cb,Ln,pb,In,hb,Sn,Jd;return l=new tt({}),F=new tt({}),Z=new tt({}),Le=new Sc({props:{pipeline:"text-classification"}}),ns=new Sc({props:{pipeline:"token-classification"}}),cs=new Sc({props:{pipeline:"fill-mask"}}),gs=new Sc({props:{pipeline:"question-answering"}}),ys=new tt({}),vs=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;
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The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
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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/main/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> or <a href="/docs/transformers/main/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;
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The range of relative positions <code>[-max_position_embeddings, max_position_embeddings]</code>. Use the same value
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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;
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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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
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Whether or not to add an initial space to the input. This allows to treat the leading word just as any
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Whether or not to add an initial &lt;|endoftext|&gt; to the input. This allows to treat the leading word just as
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`,returnType:`
<p><code>List[int]</code></p>
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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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/main/src/transformers/models/deberta/tokenization_deberta_fast.py#L74"}}),Vo=new He({props:{anchor:"transformers.DebertaTokenizerFast.example",$$slots:{default:[_T]},$$scope:{ctx:x}}}),Jo=new at({props:{$$slots:{default:[bT]},$$scope:{ctx:x}}}),Is=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/main/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>
`}}),Os=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/main/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>
`}}),Xo=new He({props:{anchor:"transformers.DebertaTokenizerFast.create_token_type_ids_from_sequences.example",$$slots:{default:[kT]},$$scope:{ctx:x}}}),Ns=new tt({}),Ws=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/main/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/main/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/main/src/transformers/models/deberta/modeling_deberta.py#L913"}}),Gs=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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/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/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/deberta/modeling_deberta.py#L937",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/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/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Zo=new at({props:{$$slots:{default:[wT]},$$scope:{ctx:x}}}),en=new He({props:{anchor:"transformers.DebertaModel.forward.example",$$slots:{default:[TT]},$$scope:{ctx:x}}}),Vs=new tt({}),Js=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/main/src/transformers/models/deberta/modeling_deberta.py#L818"}}),Xs=new tt({}),Ys=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/main/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/main/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/main/src/transformers/models/deberta/modeling_deberta.py#L1024"}}),oa=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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/deberta/modeling_deberta.py#L1043",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/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/main/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),nn=new at({props:{$$slots:{default:[yT]},$$scope:{ctx:x}}}),sn=new He({props:{anchor:"transformers.DebertaForMaskedLM.forward.example",$$slots:{default:[vT]},$$scope:{ctx:x}}}),na=new tt({}),sa=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/main/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/main/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/main/src/transformers/models/deberta/modeling_deberta.py#L1161"}}),da=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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/deberta/modeling_deberta.py#L1186",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/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/main/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),rn=new at({props:{$$slots:{default:[$T]},$$scope:{ctx:x}}}),ln=new He({props:{anchor:"transformers.DebertaForSequenceClassification.forward.example",$$slots:{default:[DT]},$$scope:{ctx:x}}}),dn=new He({props:{anchor:"transformers.DebertaForSequenceClassification.forward.example-2",$$slots:{default:[ET]},$$scope:{ctx:x}}}),ca=new tt({}),pa=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/main/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/main/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/main/src/transformers/models/deberta/modeling_deberta.py#L1279"}}),ga=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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/deberta/modeling_deberta.py#L1293",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/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/main/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),pn=new at({props:{$$slots:{default:[FT]},$$scope:{ctx:x}}}),hn=new He({props:{anchor:"transformers.DebertaForTokenClassification.forward.example",$$slots:{default:[qT]},$$scope:{ctx:x}}}),_a=new tt({}),ba=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/main/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/main/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/main/src/transformers/models/deberta/modeling_deberta.py#L1354"}}),va=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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/deberta/modeling_deberta.py#L1367",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/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/main/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput"
>transformers.modeling_outputs.QuestionAnsweringModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),mn=new at({props:{$$slots:{default:[xT]},$$scope:{ctx:x}}}),un=new He({props:{anchor:"transformers.DebertaForQuestionAnswering.forward.example",$$slots:{default:[zT]},$$scope:{ctx:x}}}),$a=new tt({}),Da=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/main/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/main/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/main/src/transformers/models/deberta/modeling_tf_deberta.py#L1093"}}),_n=new at({props:{$$slots:{default:[jT]},$$scope:{ctx:x}}}),za=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[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/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/main/src/transformers/models/deberta/modeling_tf_deberta.py#L1099",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/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/main/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutput"
>transformers.modeling_tf_outputs.TFBaseModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),bn=new at({props:{$$slots:{default:[MT]},$$scope:{ctx:x}}}),kn=new He({props:{anchor:"transformers.TFDebertaModel.call.example",$$slots:{default:[CT]},$$scope:{ctx:x}}}),ja=new tt({}),Ma=new te({props:{name:"class transformers.TFDebertaPreTrainedModel",anchor:"transformers.TFDebertaPreTrainedModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/deberta/modeling_tf_deberta.py#L993"}}),Ca=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/main/src/keras/engine/training.py#L563",returnDescription:`
<p>A tensor if there is a single output, or
a list of tensors if there are more than one outputs.</p>
`}}),Pa=new tt({}),Ra=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/main/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/main/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/main/src/transformers/models/deberta/modeling_tf_deberta.py#L1140"}}),yn=new at({props:{$$slots:{default:[AT]},$$scope:{ctx:x}}}),Sa=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[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/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/main/src/transformers/models/deberta/modeling_tf_deberta.py#L1156",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/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/main/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput"
>transformers.modeling_tf_outputs.TFMaskedLMOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),vn=new at({props:{$$slots:{default:[PT]},$$scope:{ctx:x}}}),$n=new He({props:{anchor:"transformers.TFDebertaForMaskedLM.call.example",$$slots:{default:[RT]},$$scope:{ctx:x}}}),Dn=new He({props:{anchor:"transformers.TFDebertaForMaskedLM.call.example-2",$$slots:{default:[BT]},$$scope:{ctx:x}}}),Oa=new tt({}),Na=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/main/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/main/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/main/src/transformers/models/deberta/modeling_tf_deberta.py#L1222"}}),Fn=new at({props:{$$slots:{default:[LT]},$$scope:{ctx:x}}}),Ka=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[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/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/main/src/transformers/models/deberta/modeling_tf_deberta.py#L1240",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/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/main/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),qn=new at({props:{$$slots:{default:[IT]},$$scope:{ctx:x}}}),xn=new He({props:{anchor:"transformers.TFDebertaForSequenceClassification.call.example",$$slots:{default:[ST]},$$scope:{ctx:x}}}),zn=new He({props:{anchor:"transformers.TFDebertaForSequenceClassification.call.example-2",$$slots:{default:[OT]},$$scope:{ctx:x}}}),Ga=new tt({}),Va=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/main/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/main/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/main/src/transformers/models/deberta/modeling_tf_deberta.py#L1309"}}),Mn=new at({props:{$$slots:{default:[NT]},$$scope:{ctx:x}}}),er=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[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/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/main/src/transformers/models/deberta/modeling_tf_deberta.py#L1321",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/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/main/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput"
>transformers.modeling_tf_outputs.TFTokenClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Cn=new at({props:{$$slots:{default:[WT]},$$scope:{ctx:x}}}),An=new He({props:{anchor:"transformers.TFDebertaForTokenClassification.call.example",$$slots:{default:[HT]},$$scope:{ctx:x}}}),Pn=new He({props:{anchor:"transformers.TFDebertaForTokenClassification.call.example-2",$$slots:{default:[UT]},$$scope:{ctx:x}}}),tr=new tt({}),or=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/main/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/main/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/main/src/transformers/models/deberta/modeling_tf_deberta.py#L1386"}}),Bn=new at({props:{$$slots:{default:[QT]},$$scope:{ctx:x}}}),ir=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[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/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/main/src/transformers/models/deberta/modeling_tf_deberta.py#L1397",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/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/main/en/main_classes/output#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput"
>transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Ln=new at({props:{$$slots:{default:[KT]},$$scope:{ctx:x}}}),In=new He({props:{anchor:"transformers.TFDebertaForQuestionAnswering.call.example",$$slots:{default:[GT]},$$scope:{ctx:x}}}),Sn=new He({props:{anchor:"transformers.TFDebertaForQuestionAnswering.call.example-2",$$slots:{default:[VT]},$$scope:{ctx:x}}}),{c(){d=a("meta"),b=c(),m=a("h1"),u=a("a"),k=a("span"),T(l.$$.fragment),h=c(),z=a("span"),ae=n("DeBERTa"),V=c(),B=a("h2"),oe=a("a"),se=a("span"),T(F.$$.fragment),ve=c(),G=a("span"),$e=n("Overview"),_e=c(),S=a("p"),De=n("The DeBERTa model was proposed in "),le=a("a"),J=n("DeBERTa: Decoding-enhanced BERT with Disentangled Attention"),Ee=n(` by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google\u2019s
BERT model released in 2018 and Facebook\u2019s RoBERTa model released in 2019.`),be=c(),Q=a("p"),Fe=n(`It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in
RoBERTa.`),ke=c(),K=a("p"),qe=n("The abstract from the paper is the following:"),we=c(),ne=a("p"),M=a("em"),C=n(`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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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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improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.`),dc.forEach(t),Sf=p(Qn),Qs=r(Qn,"P",{});var cc=i(Qs);Of=s(cc,"This model is also a PyTorch "),Ks=r(cc,"A",{href:!0,rel:!0});var Ek=i(Ks);Nf=s(Ek,"torch.nn.Module"),Ek.forEach(t),Wf=s(cc,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),cc.forEach(t),Hf=p(Qn),vt=r(Qn,"DIV",{class:!0});var Kn=i(vt);y(Gs.$$.fragment,Kn),Uf=p(Kn),uo=r(Kn,"P",{});var ni=i(uo);Qf=s(ni,"The "),Ir=r(ni,"A",{href:!0});var Fk=i(Ir);Kf=s(Fk,"DebertaModel"),Fk.forEach(t),Gf=s(ni," forward method, overrides the "),Qi=r(ni,"CODE",{});var qk=i(Qi);Vf=s(qk,"__call__"),qk.forEach(t),Jf=s(ni," special method."),ni.forEach(t),Xf=p(Kn),y(Zo.$$.fragment,Kn),Yf=p(Kn),y(en.$$.fragment,Kn),Kn.forEach(t),Qn.forEach(t),qd=p(o),go=r(o,"H2",{class:!0});var pc=i(go);tn=r(pc,"A",{id:!0,class:!0,href:!0});var xk=i(tn);Ki=r(xk,"SPAN",{});var zk=i(Ki);y(Vs.$$.fragment,zk),zk.forEach(t),xk.forEach(t),Zf=p(pc),Gi=r(pc,"SPAN",{});var jk=i(Gi);em=s(jk,"DebertaPreTrainedModel"),jk.forEach(t),pc.forEach(t),xd=p(o),_o=r(o,"DIV",{class:!0});var hc=i(_o);y(Js.$$.fragment,hc),tm=p(hc),Vi=r(hc,"P",{});var Mk=i(Vi);om=s(Mk,`An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.`),Mk.forEach(t),hc.forEach(t),zd=p(o),bo=r(o,"H2",{class:!0});var fc=i(bo);on=r(fc,"A",{id:!0,class:!0,href:!0});var Ck=i(on);Ji=r(Ck,"SPAN",{});var Ak=i(Ji);y(Xs.$$.fragment,Ak),Ak.forEach(t),Ck.forEach(t),nm=p(fc),Xi=r(fc,"SPAN",{});var Pk=i(Xi);sm=s(Pk,"DebertaForMaskedLM"),Pk.forEach(t),fc.forEach(t),jd=p(o),bt=r(o,"DIV",{class:!0});var Gn=i(bt);y(Ys.$$.fragment,Gn),am=p(Gn),ko=r(Gn,"P",{});var si=i(ko);rm=s(si,"DeBERTa Model with a "),Yi=r(si,"CODE",{});var Rk=i(Yi);im=s(Rk,"language modeling"),Rk.forEach(t),lm=s(si,` head on top.
The DeBERTa model was proposed in `),Zs=r(si,"A",{href:!0,rel:!0});var Bk=i(Zs);dm=s(Bk,`DeBERTa: Decoding-enhanced BERT with Disentangled
Attention`),Bk.forEach(t),cm=s(si,` by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It\u2019s build
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.`),si.forEach(t),pm=p(Gn),ea=r(Gn,"P",{});var mc=i(ea);hm=s(mc,"This model is also a PyTorch "),ta=r(mc,"A",{href:!0,rel:!0});var Lk=i(ta);fm=s(Lk,"torch.nn.Module"),Lk.forEach(t),mm=s(mc,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),mc.forEach(t),um=p(Gn),$t=r(Gn,"DIV",{class:!0});var Vn=i($t);y(oa.$$.fragment,Vn),gm=p(Vn),wo=r(Vn,"P",{});var ai=i(wo);_m=s(ai,"The "),Sr=r(ai,"A",{href:!0});var Ik=i(Sr);bm=s(Ik,"DebertaForMaskedLM"),Ik.forEach(t),km=s(ai," forward method, overrides the "),Zi=r(ai,"CODE",{});var Sk=i(Zi);wm=s(Sk,"__call__"),Sk.forEach(t),Tm=s(ai," special method."),ai.forEach(t),ym=p(Vn),y(nn.$$.fragment,Vn),vm=p(Vn),y(sn.$$.fragment,Vn),Vn.forEach(t),Gn.forEach(t),Md=p(o),To=r(o,"H2",{class:!0});var uc=i(To);an=r(uc,"A",{id:!0,class:!0,href:!0});var Ok=i(an);el=r(Ok,"SPAN",{});var Nk=i(el);y(na.$$.fragment,Nk),Nk.forEach(t),Ok.forEach(t),$m=p(uc),tl=r(uc,"SPAN",{});var Wk=i(tl);Dm=s(Wk,"DebertaForSequenceClassification"),Wk.forEach(t),uc.forEach(t),Cd=p(o),rt=r(o,"DIV",{class:!0});var Vt=i(rt);y(sa.$$.fragment,Vt),Em=p(Vt),ol=r(Vt,"P",{});var Hk=i(ol);Fm=s(Hk,`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.`),Hk.forEach(t),qm=p(Vt),aa=r(Vt,"P",{});var gc=i(aa);xm=s(gc,"The DeBERTa model was proposed in "),ra=r(gc,"A",{href:!0,rel:!0});var Uk=i(ra);zm=s(Uk,`DeBERTa: Decoding-enhanced BERT with Disentangled
Attention`),Uk.forEach(t),jm=s(gc,` 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.`),gc.forEach(t),Mm=p(Vt),ia=r(Vt,"P",{});var _c=i(ia);Cm=s(_c,"This model is also a PyTorch "),la=r(_c,"A",{href:!0,rel:!0});var Qk=i(la);Am=s(Qk,"torch.nn.Module"),Qk.forEach(t),Pm=s(_c,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),_c.forEach(t),Rm=p(Vt),pt=r(Vt,"DIV",{class:!0});var Jt=i(pt);y(da.$$.fragment,Jt),Bm=p(Jt),yo=r(Jt,"P",{});var ri=i(yo);Lm=s(ri,"The "),Or=r(ri,"A",{href:!0});var Kk=i(Or);Im=s(Kk,"DebertaForSequenceClassification"),Kk.forEach(t),Sm=s(ri," forward method, overrides the "),nl=r(ri,"CODE",{});var Gk=i(nl);Om=s(Gk,"__call__"),Gk.forEach(t),Nm=s(ri," special method."),ri.forEach(t),Wm=p(Jt),y(rn.$$.fragment,Jt),Hm=p(Jt),y(ln.$$.fragment,Jt),Um=p(Jt),y(dn.$$.fragment,Jt),Jt.forEach(t),Vt.forEach(t),Ad=p(o),vo=r(o,"H2",{class:!0});var bc=i(vo);cn=r(bc,"A",{id:!0,class:!0,href:!0});var Vk=i(cn);sl=r(Vk,"SPAN",{});var Jk=i(sl);y(ca.$$.fragment,Jk),Jk.forEach(t),Vk.forEach(t),Qm=p(bc),al=r(bc,"SPAN",{});var Xk=i(al);Km=s(Xk,"DebertaForTokenClassification"),Xk.forEach(t),bc.forEach(t),Pd=p(o),it=r(o,"DIV",{class:!0});var Xt=i(it);y(pa.$$.fragment,Xt),Gm=p(Xt),rl=r(Xt,"P",{});var Yk=i(rl);Vm=s(Yk,`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.`),Yk.forEach(t),Jm=p(Xt),ha=r(Xt,"P",{});var kc=i(ha);Xm=s(kc,"The DeBERTa model was proposed in "),fa=r(kc,"A",{href:!0,rel:!0});var Zk=i(fa);Ym=s(Zk,`DeBERTa: Decoding-enhanced BERT with Disentangled
Attention`),Zk.forEach(t),Zm=s(kc,` 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.`),kc.forEach(t),eu=p(Xt),ma=r(Xt,"P",{});var wc=i(ma);tu=s(wc,"This model is also a PyTorch "),ua=r(wc,"A",{href:!0,rel:!0});var ew=i(ua);ou=s(ew,"torch.nn.Module"),ew.forEach(t),nu=s(wc,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),wc.forEach(t),su=p(Xt),Dt=r(Xt,"DIV",{class:!0});var Jn=i(Dt);y(ga.$$.fragment,Jn),au=p(Jn),$o=r(Jn,"P",{});var ii=i($o);ru=s(ii,"The "),Nr=r(ii,"A",{href:!0});var tw=i(Nr);iu=s(tw,"DebertaForTokenClassification"),tw.forEach(t),lu=s(ii," forward method, overrides the "),il=r(ii,"CODE",{});var ow=i(il);du=s(ow,"__call__"),ow.forEach(t),cu=s(ii," special method."),ii.forEach(t),pu=p(Jn),y(pn.$$.fragment,Jn),hu=p(Jn),y(hn.$$.fragment,Jn),Jn.forEach(t),Xt.forEach(t),Rd=p(o),Do=r(o,"H2",{class:!0});var Tc=i(Do);fn=r(Tc,"A",{id:!0,class:!0,href:!0});var nw=i(fn);ll=r(nw,"SPAN",{});var sw=i(ll);y(_a.$$.fragment,sw),sw.forEach(t),nw.forEach(t),fu=p(Tc),dl=r(Tc,"SPAN",{});var aw=i(dl);mu=s(aw,"DebertaForQuestionAnswering"),aw.forEach(t),Tc.forEach(t),Bd=p(o),lt=r(o,"DIV",{class:!0});var Yt=i(lt);y(ba.$$.fragment,Yt),uu=p(Yt),Eo=r(Yt,"P",{});var li=i(Eo);gu=s(li,`DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `),cl=r(li,"CODE",{});var rw=i(cl);_u=s(rw,"span start logits"),rw.forEach(t),bu=s(li," and "),pl=r(li,"CODE",{});var iw=i(pl);ku=s(iw,"span end logits"),iw.forEach(t),wu=s(li,")."),li.forEach(t),Tu=p(Yt),ka=r(Yt,"P",{});var yc=i(ka);yu=s(yc,"The DeBERTa model was proposed in "),wa=r(yc,"A",{href:!0,rel:!0});var lw=i(wa);vu=s(lw,`DeBERTa: Decoding-enhanced BERT with Disentangled
Attention`),lw.forEach(t),$u=s(yc,` by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It\u2019s build
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.`),yc.forEach(t),Du=p(Yt),Ta=r(Yt,"P",{});var vc=i(Ta);Eu=s(vc,"This model is also a PyTorch "),ya=r(vc,"A",{href:!0,rel:!0});var dw=i(ya);Fu=s(dw,"torch.nn.Module"),dw.forEach(t),qu=s(vc,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),vc.forEach(t),xu=p(Yt),Et=r(Yt,"DIV",{class:!0});var Xn=i(Et);y(va.$$.fragment,Xn),zu=p(Xn),Fo=r(Xn,"P",{});var di=i(Fo);ju=s(di,"The "),Wr=r(di,"A",{href:!0});var cw=i(Wr);Mu=s(cw,"DebertaForQuestionAnswering"),cw.forEach(t),Cu=s(di," forward method, overrides the "),hl=r(di,"CODE",{});var pw=i(hl);Au=s(pw,"__call__"),pw.forEach(t),Pu=s(di," special method."),di.forEach(t),Ru=p(Xn),y(mn.$$.fragment,Xn),Bu=p(Xn),y(un.$$.fragment,Xn),Xn.forEach(t),Yt.forEach(t),Ld=p(o),qo=r(o,"H2",{class:!0});var $c=i(qo);gn=r($c,"A",{id:!0,class:!0,href:!0});var hw=i(gn);fl=r(hw,"SPAN",{});var fw=i(fl);y($a.$$.fragment,fw),fw.forEach(t),hw.forEach(t),Lu=p($c),ml=r($c,"SPAN",{});var mw=i(ml);Iu=s(mw,"TFDebertaModel"),mw.forEach(t),$c.forEach(t),Id=p(o),dt=r(o,"DIV",{class:!0});var Zt=i(dt);y(Da.$$.fragment,Zt),Su=p(Zt),Ea=r(Zt,"P",{});var Dc=i(Ea);Ou=s(Dc,`The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.
The DeBERTa model was proposed in `),Fa=r(Dc,"A",{href:!0,rel:!0});var uw=i(Fa);Nu=s(uw,`DeBERTa: Decoding-enhanced BERT with Disentangled
Attention`),uw.forEach(t),Wu=s(Dc,` 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.`),Dc.forEach(t),Hu=p(Zt),qa=r(Zt,"P",{});var Ec=i(qa);Uu=s(Ec,"This model is also a "),xa=r(Ec,"A",{href:!0,rel:!0});var gw=i(xa);Qu=s(gw,"tf.keras.Model"),gw.forEach(t),Ku=s(Ec,` 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.`),Ec.forEach(t),Gu=p(Zt),y(_n.$$.fragment,Zt),Vu=p(Zt),Ft=r(Zt,"DIV",{class:!0});var Yn=i(Ft);y(za.$$.fragment,Yn),Ju=p(Yn),xo=r(Yn,"P",{});var ci=i(xo);Xu=s(ci,"The "),Hr=r(ci,"A",{href:!0});var _w=i(Hr);Yu=s(_w,"TFDebertaModel"),_w.forEach(t),Zu=s(ci," forward method, overrides the "),ul=r(ci,"CODE",{});var bw=i(ul);eg=s(bw,"__call__"),bw.forEach(t),tg=s(ci," special method."),ci.forEach(t),og=p(Yn),y(bn.$$.fragment,Yn),ng=p(Yn),y(kn.$$.fragment,Yn),Yn.forEach(t),Zt.forEach(t),Sd=p(o),zo=r(o,"H2",{class:!0});var Fc=i(zo);wn=r(Fc,"A",{id:!0,class:!0,href:!0});var kw=i(wn);gl=r(kw,"SPAN",{});var ww=i(gl);y(ja.$$.fragment,ww),ww.forEach(t),kw.forEach(t),sg=p(Fc),_l=r(Fc,"SPAN",{});var Tw=i(_l);ag=s(Tw,"TFDebertaPreTrainedModel"),Tw.forEach(t),Fc.forEach(t),Od=p(o),Ct=r(o,"DIV",{class:!0});var pi=i(Ct);y(Ma.$$.fragment,pi),rg=p(pi),bl=r(pi,"P",{});var yw=i(bl);ig=s(yw,`An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.`),yw.forEach(t),lg=p(pi),qt=r(pi,"DIV",{class:!0});var Zn=i(qt);y(Ca.$$.fragment,Zn),dg=p(Zn),kl=r(Zn,"P",{});var vw=i(kl);cg=s(vw,"Calls the model on new inputs and returns the outputs as tensors."),vw.forEach(t),pg=p(Zn),Aa=r(Zn,"P",{});var qc=i(Aa);hg=s(qc,"In this case "),wl=r(qc,"CODE",{});var $w=i(wl);fg=s($w,"call()"),$w.forEach(t),mg=s(qc,` just reapplies
all ops in the graph to the new inputs
(e.g. build a new computational graph from the provided inputs).`),qc.forEach(t),ug=p(Zn),kt=r(Zn,"P",{});var eo=i(kt);gg=s(eo,`Note: This method should not be called directly. It is only meant to be
overridden when subclassing `),Tl=r(eo,"CODE",{});var Dw=i(Tl);_g=s(Dw,"tf.keras.Model"),Dw.forEach(t),bg=s(eo,`.
To call a model on an input, always use the `),yl=r(eo,"CODE",{});var Ew=i(yl);kg=s(Ew,"__call__()"),Ew.forEach(t),wg=s(eo,` method,
i.e. `),vl=r(eo,"CODE",{});var Fw=i(vl);Tg=s(Fw,"model(inputs)"),Fw.forEach(t),yg=s(eo,", which relies on the underlying "),$l=r(eo,"CODE",{});var qw=i($l);vg=s(qw,"call()"),qw.forEach(t),$g=s(eo," method."),eo.forEach(t),Zn.forEach(t),pi.forEach(t),Nd=p(o),jo=r(o,"H2",{class:!0});var xc=i(jo);Tn=r(xc,"A",{id:!0,class:!0,href:!0});var xw=i(Tn);Dl=r(xw,"SPAN",{});var zw=i(Dl);y(Pa.$$.fragment,zw),zw.forEach(t),xw.forEach(t),Dg=p(xc),El=r(xc,"SPAN",{});var jw=i(El);Eg=s(jw,"TFDebertaForMaskedLM"),jw.forEach(t),xc.forEach(t),Wd=p(o),ct=r(o,"DIV",{class:!0});var to=i(ct);y(Ra.$$.fragment,to),Fg=p(to),Mo=r(to,"P",{});var hi=i(Mo);qg=s(hi,"DeBERTa Model with a "),Fl=r(hi,"CODE",{});var Mw=i(Fl);xg=s(Mw,"language modeling"),Mw.forEach(t),zg=s(hi,` head on top.
The DeBERTa model was proposed in `),Ba=r(hi,"A",{href:!0,rel:!0});var Cw=i(Ba);jg=s(Cw,`DeBERTa: Decoding-enhanced BERT with Disentangled
Attention`),Cw.forEach(t),Mg=s(hi,` by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It\u2019s build
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.`),hi.forEach(t),Cg=p(to),La=r(to,"P",{});var zc=i(La);Ag=s(zc,"This model is also a "),Ia=r(zc,"A",{href:!0,rel:!0});var Aw=i(Ia);Pg=s(Aw,"tf.keras.Model"),Aw.forEach(t),Rg=s(zc,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),zc.forEach(t),Bg=p(to),y(yn.$$.fragment,to),Lg=p(to),ht=r(to,"DIV",{class:!0});var oo=i(ht);y(Sa.$$.fragment,oo),Ig=p(oo),Co=r(oo,"P",{});var fi=i(Co);Sg=s(fi,"The "),Ur=r(fi,"A",{href:!0});var Pw=i(Ur);Og=s(Pw,"TFDebertaForMaskedLM"),Pw.forEach(t),Ng=s(fi," forward method, overrides the "),ql=r(fi,"CODE",{});var Rw=i(ql);Wg=s(Rw,"__call__"),Rw.forEach(t),Hg=s(fi," special method."),fi.forEach(t),Ug=p(oo),y(vn.$$.fragment,oo),Qg=p(oo),y($n.$$.fragment,oo),Kg=p(oo),y(Dn.$$.fragment,oo),oo.forEach(t),to.forEach(t),Hd=p(o),Ao=r(o,"H2",{class:!0});var jc=i(Ao);En=r(jc,"A",{id:!0,class:!0,href:!0});var Bw=i(En);xl=r(Bw,"SPAN",{});var Lw=i(xl);y(Oa.$$.fragment,Lw),Lw.forEach(t),Bw.forEach(t),Gg=p(jc),zl=r(jc,"SPAN",{});var Iw=i(zl);Vg=s(Iw,"TFDebertaForSequenceClassification"),Iw.forEach(t),jc.forEach(t),Ud=p(o),ot=r(o,"DIV",{class:!0});var xt=i(ot);y(Na.$$.fragment,xt),Jg=p(xt),jl=r(xt,"P",{});var Sw=i(jl);Xg=s(Sw,`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.`),Sw.forEach(t),Yg=p(xt),Wa=r(xt,"P",{});var Mc=i(Wa);Zg=s(Mc,"The DeBERTa model was proposed in "),Ha=r(Mc,"A",{href:!0,rel:!0});var Ow=i(Ha);e_=s(Ow,`DeBERTa: Decoding-enhanced BERT with Disentangled
Attention`),Ow.forEach(t),t_=s(Mc,` 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.`),Mc.forEach(t),o_=p(xt),Ua=r(xt,"P",{});var Cc=i(Ua);n_=s(Cc,"This model is also a "),Qa=r(Cc,"A",{href:!0,rel:!0});var Nw=i(Qa);s_=s(Nw,"tf.keras.Model"),Nw.forEach(t),a_=s(Cc,` 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.`),Cc.forEach(t),r_=p(xt),y(Fn.$$.fragment,xt),i_=p(xt),ft=r(xt,"DIV",{class:!0});var no=i(ft);y(Ka.$$.fragment,no),l_=p(no),Po=r(no,"P",{});var mi=i(Po);d_=s(mi,"The "),Qr=r(mi,"A",{href:!0});var Ww=i(Qr);c_=s(Ww,"TFDebertaForSequenceClassification"),Ww.forEach(t),p_=s(mi," forward method, overrides the "),Ml=r(mi,"CODE",{});var Hw=i(Ml);h_=s(Hw,"__call__"),Hw.forEach(t),f_=s(mi," special method."),mi.forEach(t),m_=p(no),y(qn.$$.fragment,no),u_=p(no),y(xn.$$.fragment,no),g_=p(no),y(zn.$$.fragment,no),no.forEach(t),xt.forEach(t),Qd=p(o),Ro=r(o,"H2",{class:!0});var Ac=i(Ro);jn=r(Ac,"A",{id:!0,class:!0,href:!0});var Uw=i(jn);Cl=r(Uw,"SPAN",{});var Qw=i(Cl);y(Ga.$$.fragment,Qw),Qw.forEach(t),Uw.forEach(t),__=p(Ac),Al=r(Ac,"SPAN",{});var Kw=i(Al);b_=s(Kw,"TFDebertaForTokenClassification"),Kw.forEach(t),Ac.forEach(t),Kd=p(o),nt=r(o,"DIV",{class:!0});var zt=i(nt);y(Va.$$.fragment,zt),k_=p(zt),Pl=r(zt,"P",{});var Gw=i(Pl);w_=s(Gw,`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.`),Gw.forEach(t),T_=p(zt),Ja=r(zt,"P",{});var Pc=i(Ja);y_=s(Pc,"The DeBERTa model was proposed in "),Xa=r(Pc,"A",{href:!0,rel:!0});var Vw=i(Xa);v_=s(Vw,`DeBERTa: Decoding-enhanced BERT with Disentangled
Attention`),Vw.forEach(t),$_=s(Pc,` 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
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