import{S as Vw,i as Jw,s as Kw,e as n,k as p,w as B,t as a,M as Xw,c as s,d as t,m as h,a as r,x as v,h as i,b as c,G as e,g as k,y,q as T,o as $,B as w,v as Yw,L as E}from"../../chunks/vendor-hf-doc-builder.js";import{T as R}from"../../chunks/Tip-hf-doc-builder.js";import{D as z}from"../../chunks/Docstring-hf-doc-builder.js";import{C as j}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as q}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as M}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Zw(x){let d,_,g,f,b;return f=new j({props:{code:`from transformers import BigBirdConfig, BigBirdModel

# Initializing a BigBird google/bigbird-roberta-base style configuration
configuration = BigBirdConfig()

# Initializing a model (with random weights) from the google/bigbird-roberta-base style configuration
model = BigBirdModel(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> BigBirdConfig, BigBirdModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a BigBird google/bigbird-roberta-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = BigBirdConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the google/bigbird-roberta-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdModel(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=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function e0(x){let d,_,g,f,b;return f=new j({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=n("p"),_=a("sequence pair mask has the following format:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"sequence pair mask has the following format:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function t0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function o0(x){let d,_,g,f,b;return f=new j({props:{code:`from transformers import BigBirdTokenizer, BigBirdModel
import torch

tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = BigBirdModel.from_pretrained("google/bigbird-roberta-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> BigBirdTokenizer, BigBirdModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdModel.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-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=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function n0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function s0(x){let d,_,g,f,b;return f=new j({props:{code:`from transformers import BigBirdTokenizer, BigBirdForPreTraining
import torch

tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = BigBirdForPreTraining.from_pretrained("google/bigbird-roberta-base")

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

prediction_logits = outputs.prediction_logits
seq_relationship_logits = outputs.seq_relationship_logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BigBirdTokenizer, BigBirdForPreTraining
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForPreTraining.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-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>prediction_logits = outputs.prediction_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>seq_relationship_logits = outputs.seq_relationship_logits`}}),{c(){d=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function r0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function a0(x){let d,_,g,f,b;return f=new j({props:{code:`import torch
from transformers import BigBirdTokenizer, BigBirdForCausalLM

tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = BigBirdForCausalLM.from_pretrained("google/bigbird-roberta-base")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
loss = outputs.loss
logits = outputs.logits`,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> BigBirdTokenizer, BigBirdForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForCausalLM.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-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, labels=inputs[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function i0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function l0(x){let d,_,g,f,b;return f=new j({props:{code:`import torch
from transformers import BigBirdTokenizer, BigBirdForMaskedLM
from datasets import load_dataset

tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = BigBirdForMaskedLM.from_pretrained("google/bigbird-roberta-base")
squad_ds = load_dataset("squad_v2", split="train")
# select random long article
LONG_ARTICLE_TARGET = squad_ds[81514]["context"]
# select random sentence
LONG_ARTICLE_TARGET[332:398]

# add mask_token
LONG_ARTICLE_TO_MASK = LONG_ARTICLE_TARGET.replace("maximum", "[MASK]")
inputs = tokenizer(LONG_ARTICLE_TO_MASK, return_tensors="pt")
# long article input
list(inputs["input_ids"].shape)

with torch.no_grad():
    logits = model(**inputs).logits
# retrieve index of [MASK]
mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
tokenizer.decode(predicted_token_id)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BigBirdTokenizer, BigBirdForMaskedLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>squad_ds = load_dataset(<span class="hljs-string">&quot;squad_v2&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># select random long article</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>LONG_ARTICLE_TARGET = squad_ds[<span class="hljs-number">81514</span>][<span class="hljs-string">&quot;context&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># select random sentence</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>LONG_ARTICLE_TARGET[<span class="hljs-number">332</span>:<span class="hljs-number">398</span>]
<span class="hljs-string">&#x27;the highest values are very close to the theoretical maximum value&#x27;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># add mask_token</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>LONG_ARTICLE_TO_MASK = LONG_ARTICLE_TARGET.replace(<span class="hljs-string">&quot;maximum&quot;</span>, <span class="hljs-string">&quot;[MASK]&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(LONG_ARTICLE_TO_MASK, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># long article input</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(inputs[<span class="hljs-string">&quot;input_ids&quot;</span>].shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">919</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;maximum&#x27;</span>`}}),{c(){d=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function d0(x){let d,_;return d=new j({props:{code:`labels = tokenizer(LONG_ARTICLE_TARGET, return_tensors="pt")["input_ids"]
labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
outputs = model(**inputs, labels=labels)
round(outputs.loss.item(), 2)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tokenizer(LONG_ARTICLE_TARGET, 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>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">1.08</span>`}}),{c(){B(d.$$.fragment)},l(g){v(d.$$.fragment,g)},m(g,f){y(d,g,f),_=!0},p:E,i(g){_||(T(d.$$.fragment,g),_=!0)},o(g){$(d.$$.fragment,g),_=!1},d(g){w(d,g)}}}function c0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function p0(x){let d,_,g,f,b;return f=new j({props:{code:`import torch
from transformers import BigBirdTokenizer, BigBirdForSequenceClassification
from datasets import load_dataset

tokenizer = BigBirdTokenizer.from_pretrained("l-yohai/bigbird-roberta-base-mnli")
model = BigBirdForSequenceClassification.from_pretrained("l-yohai/bigbird-roberta-base-mnli")
squad_ds = load_dataset("squad_v2", split="train")
LONG_ARTICLE = squad_ds[81514]["context"]
inputs = tokenizer(LONG_ARTICLE, return_tensors="pt")
# long input article
list(inputs["input_ids"].shape)

with torch.no_grad():
    logits = model(**inputs).logits
predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BigBirdTokenizer, BigBirdForSequenceClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;l-yohai/bigbird-roberta-base-mnli&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;l-yohai/bigbird-roberta-base-mnli&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>squad_ds = load_dataset(<span class="hljs-string">&quot;squad_v2&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>LONG_ARTICLE = squad_ds[<span class="hljs-number">81514</span>][<span class="hljs-string">&quot;context&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(LONG_ARTICLE, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># long input article</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(inputs[<span class="hljs-string">&quot;input_ids&quot;</span>].shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">919</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_id = logits.argmax().item()
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.id2label[predicted_class_id]
<span class="hljs-string">&#x27;LABEL_0&#x27;</span>`}}),{c(){d=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function h0(x){let d,_;return d=new j({props:{code:`num_labels = len(model.config.id2label)
model = BigBirdForSequenceClassification.from_pretrained(
    "l-yohai/bigbird-roberta-base-mnli", num_labels=num_labels
)
labels = torch.tensor(1)
loss = model(**inputs, labels=labels).loss
round(loss.item(), 2)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>num_labels = <span class="hljs-built_in">len</span>(model.config.id2label)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;l-yohai/bigbird-roberta-base-mnli&quot;</span>, num_labels=num_labels
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = torch.tensor(<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(loss.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">1.13</span>`}}),{c(){B(d.$$.fragment)},l(g){v(d.$$.fragment,g)},m(g,f){y(d,g,f),_=!0},p:E,i(g){_||(T(d.$$.fragment,g),_=!0)},o(g){$(d.$$.fragment,g),_=!1},d(g){w(d,g)}}}function m0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function g0(x){let d,_,g,f,b;return f=new j({props:{code:`from transformers import BigBirdTokenizer, BigBirdForMultipleChoice
import torch

tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = BigBirdForMultipleChoice.from_pretrained("google/bigbird-roberta-base")

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># the linear classifier still needs to be trained</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function f0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function u0(x){let d,_,g,f,b;return f=new j({props:{code:`from transformers import BigBirdTokenizer, BigBirdForTokenClassification
import torch

tokenizer = BigBirdTokenizer.from_pretrained("vumichien/token-classification-bigbird-roberta-base-random")
model = BigBirdForTokenClassification.from_pretrained("vumichien/token-classification-bigbird-roberta-base-random")

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

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

predicted_token_class_ids = logits.argmax(-1)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;vumichien/token-classification-bigbird-roberta-base-random&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForTokenClassification.from_pretrained(<span class="hljs-string">&quot;vumichien/token-classification-bigbird-roberta-base-random&quot;</span>)

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Note that tokens are classified rather then input words which means that</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># there might be more predicted token classes than words.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Multiple token classes might account for the same word</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_tokens_classes = [model.config.id2label[t.item()] <span class="hljs-keyword">for</span> t <span class="hljs-keyword">in</span> predicted_token_class_ids[<span class="hljs-number">0</span>]]
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_tokens_classes
[<span class="hljs-string">&#x27;LABEL_1&#x27;</span>, <span class="hljs-string">&#x27;LABEL_1&#x27;</span>, <span class="hljs-string">&#x27;LABEL_1&#x27;</span>, <span class="hljs-string">&#x27;LABEL_1&#x27;</span>, <span class="hljs-string">&#x27;LABEL_1&#x27;</span>, <span class="hljs-string">&#x27;LABEL_1&#x27;</span>, <span class="hljs-string">&#x27;LABEL_1&#x27;</span>, <span class="hljs-string">&#x27;LABEL_1&#x27;</span>, <span class="hljs-string">&#x27;LABEL_1&#x27;</span>, <span class="hljs-string">&#x27;LABEL_1&#x27;</span>, <span class="hljs-string">&#x27;LABEL_1&#x27;</span>, <span class="hljs-string">&#x27;LABEL_1&#x27;</span>]`}}),{c(){d=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function _0(x){let d,_;return d=new j({props:{code:`labels = predicted_token_class_ids
loss = model(**inputs, labels=labels).loss
round(loss.item(), 2)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>labels = predicted_token_class_ids
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(loss.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">0.54</span>`}}),{c(){B(d.$$.fragment)},l(g){v(d.$$.fragment,g)},m(g,f){y(d,g,f),_=!0},p:E,i(g){_||(T(d.$$.fragment,g),_=!0)},o(g){$(d.$$.fragment,g),_=!1},d(g){w(d,g)}}}function b0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function k0(x){let d,_,g,f,b;return f=new j({props:{code:`import torch
from transformers import BigBirdTokenizer, BigBirdForQuestionAnswering
from datasets import load_dataset

tokenizer = BigBirdTokenizer.from_pretrained("abhinavkulkarni/bigbird-roberta-base-finetuned-squad")
model = BigBirdForQuestionAnswering.from_pretrained("abhinavkulkarni/bigbird-roberta-base-finetuned-squad")
squad_ds = load_dataset("squad_v2", split="train")
# select random article and question
LONG_ARTICLE = squad_ds[81514]["context"]
QUESTION = squad_ds[81514]["question"]
QUESTION

inputs = tokenizer(QUESTION, LONG_ARTICLE, return_tensors="pt")
# long article and question input
list(inputs["input_ids"].shape)

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)`,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> BigBirdTokenizer, BigBirdForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;abhinavkulkarni/bigbird-roberta-base-finetuned-squad&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;abhinavkulkarni/bigbird-roberta-base-finetuned-squad&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>squad_ds = load_dataset(<span class="hljs-string">&quot;squad_v2&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># select random article and question</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>LONG_ARTICLE = squad_ds[<span class="hljs-number">81514</span>][<span class="hljs-string">&quot;context&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>QUESTION = squad_ds[<span class="hljs-number">81514</span>][<span class="hljs-string">&quot;question&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>QUESTION
<span class="hljs-string">&#x27;During daytime how high can the temperatures reach?&#x27;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(QUESTION, LONG_ARTICLE, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># long article and question input</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(inputs[<span class="hljs-string">&quot;input_ids&quot;</span>].shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">929</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)
<span class="hljs-string">&#x27;80 \xB0C (176 \xB0F) or more&#x27;</span>`}}),{c(){d=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function B0(x){let d,_;return d=new j({props:{code:`target_start_index, target_end_index = torch.tensor([130]), torch.tensor([132])
outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
loss = outputs.loss
round(outputs.loss.item(), 2)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>target_start_index, target_end_index = torch.tensor([<span class="hljs-number">130</span>]), torch.tensor([<span class="hljs-number">132</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>(outputs.loss.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">7.63</span>`}}),{c(){B(d.$$.fragment)},l(g){v(d.$$.fragment,g)},m(g,f){y(d,g,f),_=!0},p:E,i(g){_||(T(d.$$.fragment,g),_=!0)},o(g){$(d.$$.fragment,g),_=!1},d(g){w(d,g)}}}function v0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function y0(x){let d,_,g,f,b;return f=new j({props:{code:`from transformers import BigBirdTokenizer, FlaxBigBirdModel

tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = FlaxBigBirdModel.from_pretrained("google/bigbird-roberta-base")

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdModel.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-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;jax&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){d=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function T0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function $0(x){let d,_,g,f,b;return f=new j({props:{code:`from transformers import BigBirdTokenizer, FlaxBigBirdForPreTraining

tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = FlaxBigBirdForPreTraining.from_pretrained("google/bigbird-roberta-base")

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

prediction_logits = outputs.prediction_logits
seq_relationship_logits = outputs.seq_relationship_logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BigBirdTokenizer, FlaxBigBirdForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdForPreTraining.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-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;np&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>prediction_logits = outputs.prediction_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>seq_relationship_logits = outputs.seq_relationship_logits`}}),{c(){d=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function w0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function x0(x){let d,_,g,f,b;return f=new j({props:{code:`from transformers import BigBirdTokenizer, FlaxBigBirdForCausalLM

tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = FlaxBigBirdForCausalLM.from_pretrained("google/bigbird-roberta-base")

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdForCausalLM.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-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;np&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># retrieve logts for next token</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>next_token_logits = outputs.logits[:, -<span class="hljs-number">1</span>]`}}),{c(){d=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function F0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function z0(x){let d,_,g,f,b;return f=new j({props:{code:`from transformers import BigBirdTokenizer, FlaxBigBirdForMaskedLM

tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = FlaxBigBirdForMaskedLM.from_pretrained("google/bigbird-roberta-base")

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-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;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function q0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function M0(x){let d,_,g,f,b;return f=new j({props:{code:`from transformers import BigBirdTokenizer, FlaxBigBirdForSequenceClassification

tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = FlaxBigBirdForSequenceClassification.from_pretrained("google/bigbird-roberta-base")

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-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;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function E0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function j0(x){let d,_,g,f,b;return f=new j({props:{code:`from transformers import BigBirdTokenizer, FlaxBigBirdForMultipleChoice

tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = FlaxBigBirdForMultipleChoice.from_pretrained("google/bigbird-roberta-base")

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function P0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function C0(x){let d,_,g,f,b;return f=new j({props:{code:`from transformers import BigBirdTokenizer, FlaxBigBirdForTokenClassification

tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = FlaxBigBirdForTokenClassification.from_pretrained("google/bigbird-roberta-base")

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-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;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=n("p"),_=a("Example:"),g=p(),B(f.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),v(f.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(f,l,m),b=!0},p:E,i(l){b||(T(f.$$.fragment,l),b=!0)},o(l){$(f.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(f,l)}}}function L0(x){let d,_,g,f,b;return{c(){d=n("p"),_=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),f=a("Module"),b=a(`
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=s(l,"P",{});var m=r(d);_=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(m,"CODE",{});var F=r(g);f=i(F,"Module"),F.forEach(t),b=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(t)},m(l,m){k(l,d,m),e(d,_),e(d,g),e(g,f),e(d,b)},d(l){l&&t(d)}}}function A0(x){let d,_,g,f,b;return f=new j({props:{code:`from transformers import BigBirdTokenizer, FlaxBigBirdForQuestionAnswering

tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = FlaxBigBirdForQuestionAnswering.from_pretrained("google/bigbird-roberta-base")

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BigBirdTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-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;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>start_scores = outputs.start_logits
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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
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The vocabulary size of the <code>token_type_ids</code> passed when calling <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a>.`,name:"type_vocab_size"},{anchor:"transformers.BigBirdConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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Whether to use block sparse attention (with n complexity) as introduced in paper or original attention
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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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 masking values. This is the token used when training this model with masked language
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SentencePiece</a> can be used, among other things,
to set:</p>
<ul>
<li>
<p><code>enable_sampling</code>: Enable subword regularization.</p>
</li>
<li>
<p><code>nbest_size</code>: Sampling parameters for unigram. Invalid for BPE-Dropout.</p>
<ul>
<li><code>nbest_size = {0,1}</code>: No sampling is performed.</li>
<li><code>nbest_size &gt; 1</code>: samples from the nbest_size results.</li>
<li><code>nbest_size &lt; 0</code>: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.</li>
</ul>
</li>
<li>
<p><code>alpha</code>: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.</p>
</li>
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`,returnType:`
<p><code>List[int]</code></p>
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`,returnType:`
<p><code>List[int]</code></p>
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<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>
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<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.BigBirdTokenizerFast.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&quot;</code>) &#x2014;
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the <code>cls_token</code>.</p>

					</div>`,name:"bos_token"},{anchor:"transformers.BigBirdTokenizerFast.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token. .. note:: When building a sequence using special tokens, this is not the token
that is used for the end of sequence. The token used is the <code>sep_token</code>.`,name:"eos_token"},{anchor:"transformers.BigBirdTokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.BigBirdTokenizerFast.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.`,name:"sep_token"},{anchor:"transformers.BigBirdTokenizerFast.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.BigBirdTokenizerFast.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.BigBirdTokenizerFast.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[MASK]&quot;</code>) &#x2014;
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.`,name:"mask_token"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/tokenization_big_bird_fast.py#L68"}}),Xn=new z({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.BigBirdTokenizerFast.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.BigBirdTokenizerFast.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.BigBirdTokenizerFast.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/vr_19907/src/transformers/models/big_bird/tokenization_big_bird_fast.py#L155",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>
`}}),Zn=new z({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.BigBirdTokenizerFast.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.BigBirdTokenizerFast.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.BigBirdTokenizerFast.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/vr_19907/src/transformers/models/big_bird/tokenization_big_bird_fast.py#L211",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>
`}}),Yt=new M({props:{anchor:"transformers.BigBirdTokenizerFast.create_token_type_ids_from_sequences.example",$$slots:{default:[e0]},$$scope:{ctx:x}}}),es=new z({props:{name:"get_special_tokens_mask",anchor:"transformers.BigBirdTokenizerFast.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.BigBirdTokenizerFast.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of ids.`,name:"token_ids_0"},{anchor:"transformers.BigBirdTokenizerFast.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"},{anchor:"transformers.BigBirdTokenizerFast.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Set to True if the token list is already formatted with special tokens for the model`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/tokenization_big_bird_fast.py#L180",returnDescription:`
<p>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),os=new q({}),ns=new z({props:{name:"class transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput",anchor:"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput",parameters:[{name:"loss",val:": typing.Optional[torch.FloatTensor] = None"},{name:"prediction_logits",val:": FloatTensor = None"},{name:"seq_relationship_logits",val:": FloatTensor = None"},{name:"hidden_states",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"attentions",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}],parametersDescription:[{anchor:"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput.loss",description:`<strong>loss</strong> (<em>optional</em>, returned when <code>labels</code> is provided, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) &#x2014;
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.`,name:"loss"},{anchor:"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput.prediction_logits",description:`<strong>prediction_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) &#x2014;
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).`,name:"prediction_logits"},{anchor:"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput.seq_relationship_logits",description:`<strong>seq_relationship_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2)</code>) &#x2014;
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).`,name:"seq_relationship_logits"},{anchor:"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput.hidden_states",description:`<strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.`,name:"hidden_states"},{anchor:"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput.attentions",description:`<strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.`,name:"attentions"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L1852"}}),rs=new q({}),as=new z({props:{name:"class transformers.BigBirdModel",anchor:"transformers.BigBirdModel",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = True"}],parametersDescription:[{anchor:"transformers.BigBirdModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/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/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L1924"}}),ps=new z({props:{name:"forward",anchor:"transformers.BigBirdModel.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BigBirdModel.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/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.BigBirdModel.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.BigBirdModel.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.BigBirdModel.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.BigBirdModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BigBirdModel.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.BigBirdModel.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.BigBirdModel.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.BigBirdModel.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/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdModel.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.BigBirdModel.forward.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"encoder_attention_mask"},{anchor:"transformers.BigBirdModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.BigBirdModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L1981",returnDescription:`
<p>A <a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> and <code>config.add_cross_attention=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and optionally if
<code>config.is_encoder_decoder=True</code> 2 additional tensors of shape <code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
<code>config.is_encoder_decoder=True</code> in the cross-attention blocks) that can be used (see <code>past_key_values</code>
input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),oo=new R({props:{$$slots:{default:[t0]},$$scope:{ctx:x}}}),no=new M({props:{anchor:"transformers.BigBirdModel.forward.example",$$slots:{default:[o0]},$$scope:{ctx:x}}}),hs=new q({}),ms=new z({props:{name:"class transformers.BigBirdForPreTraining",anchor:"transformers.BigBirdForPreTraining",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L2264"}}),gs=new z({props:{name:"forward",anchor:"transformers.BigBirdForPreTraining.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.FloatTensor] = None"},{name:"next_sentence_label",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BigBirdForPreTraining.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/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.BigBirdForPreTraining.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.BigBirdForPreTraining.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.BigBirdForPreTraining.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.BigBirdForPreTraining.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BigBirdForPreTraining.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.BigBirdForPreTraining.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.BigBirdForPreTraining.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.BigBirdForPreTraining.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/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdForPreTraining.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are ignored (masked), the
loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"},{anchor:"transformers.BigBirdForPreTraining.forward.next_sentence_label",description:`<strong>next_sentence_label</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the next sequence prediction (classification) loss. If specified, nsp loss will be
added to masked_lm loss. Input should be a sequence pair (see <code>input_ids</code> docstring) Indices should be in
<code>[0, 1]</code>:</p>
<ul>
<li>0 indicates sequence B is a continuation of sequence A,</li>
<li>1 indicates sequence B is a random sequence.</li>
</ul>`,name:"next_sentence_label"},{anchor:"transformers.BigBirdForPreTraining.forward.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, any]</code>, optional, defaults to <em>{}</em>) &#x2014;
Used to hide legacy arguments that have been deprecated.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L2280",returnDescription:`
<p>A <a
  href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput"
>transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput</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/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<em>optional</em>, returned when <code>labels</code> is provided, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) \u2014 Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.</p>
</li>
<li>
<p><strong>prediction_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>seq_relationship_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2)</code>) \u2014 Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput"
>transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ro=new R({props:{$$slots:{default:[n0]},$$scope:{ctx:x}}}),ao=new M({props:{anchor:"transformers.BigBirdForPreTraining.forward.example",$$slots:{default:[s0]},$$scope:{ctx:x}}}),fs=new q({}),us=new z({props:{name:"class transformers.BigBirdForCausalLM",anchor:"transformers.BigBirdForCausalLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BigBirdForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/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/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L2510"}}),bs=new z({props:{name:"forward",anchor:"transformers.BigBirdForCausalLM.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BigBirdForCausalLM.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/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.BigBirdForCausalLM.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.BigBirdForCausalLM.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.BigBirdForCausalLM.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.BigBirdForCausalLM.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BigBirdForCausalLM.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.BigBirdForCausalLM.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.BigBirdForCausalLM.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.BigBirdForCausalLM.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/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdForCausalLM.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.BigBirdForCausalLM.forward.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"encoder_attention_mask"},{anchor:"transformers.BigBirdForCausalLM.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.BigBirdForCausalLM.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
<code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are
ignored (masked), the loss is only computed for the tokens with labels n <code>[0, ..., config.vocab_size]</code>.`,name:"labels"},{anchor:"transformers.BigBirdForCausalLM.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L2532",returnDescription:`
<p>A <a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Language modeling loss (for next-token prediction).</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),lo=new R({props:{$$slots:{default:[r0]},$$scope:{ctx:x}}}),co=new M({props:{anchor:"transformers.BigBirdForCausalLM.forward.example",$$slots:{default:[a0]},$$scope:{ctx:x}}}),ks=new q({}),Bs=new z({props:{name:"class transformers.BigBirdForMaskedLM",anchor:"transformers.BigBirdForMaskedLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BigBirdForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/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/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L2368"}}),ys=new z({props:{name:"forward",anchor:"transformers.BigBirdForMaskedLM.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BigBirdForMaskedLM.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/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.BigBirdForMaskedLM.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.BigBirdForMaskedLM.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.BigBirdForMaskedLM.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.BigBirdForMaskedLM.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BigBirdForMaskedLM.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.BigBirdForMaskedLM.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.BigBirdForMaskedLM.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.BigBirdForMaskedLM.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/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdForMaskedLM.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/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L2390",returnDescription:`
<p>A <a
  href="/docs/transformers/pr_19907/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/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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/pr_19907/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ho=new R({props:{$$slots:{default:[i0]},$$scope:{ctx:x}}}),mo=new M({props:{anchor:"transformers.BigBirdForMaskedLM.forward.example",$$slots:{default:[l0]},$$scope:{ctx:x}}}),go=new M({props:{anchor:"transformers.BigBirdForMaskedLM.forward.example-2",$$slots:{default:[d0]},$$scope:{ctx:x}}}),Ts=new q({}),$s=new z({props:{name:"class transformers.BigBirdForSequenceClassification",anchor:"transformers.BigBirdForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BigBirdForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/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/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L2674"}}),Fs=new z({props:{name:"forward",anchor:"transformers.BigBirdForSequenceClassification.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BigBirdForSequenceClassification.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/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.BigBirdForSequenceClassification.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.BigBirdForSequenceClassification.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.BigBirdForSequenceClassification.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.BigBirdForSequenceClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BigBirdForSequenceClassification.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.BigBirdForSequenceClassification.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.BigBirdForSequenceClassification.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.BigBirdForSequenceClassification.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/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdForSequenceClassification.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/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L2685",returnDescription:`
<p>A <a
  href="/docs/transformers/pr_19907/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/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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/pr_19907/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),uo=new R({props:{$$slots:{default:[c0]},$$scope:{ctx:x}}}),_o=new M({props:{anchor:"transformers.BigBirdForSequenceClassification.forward.example",$$slots:{default:[p0]},$$scope:{ctx:x}}}),bo=new M({props:{anchor:"transformers.BigBirdForSequenceClassification.forward.example-2",$$slots:{default:[h0]},$$scope:{ctx:x}}}),zs=new q({}),qs=new z({props:{name:"class transformers.BigBirdForMultipleChoice",anchor:"transformers.BigBirdForMultipleChoice",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BigBirdForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/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/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L2802"}}),js=new z({props:{name:"forward",anchor:"transformers.BigBirdForMultipleChoice.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BigBirdForMultipleChoice.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.BigBirdForMultipleChoice.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.BigBirdForMultipleChoice.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.BigBirdForMultipleChoice.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.BigBirdForMultipleChoice.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BigBirdForMultipleChoice.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.BigBirdForMultipleChoice.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.BigBirdForMultipleChoice.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.BigBirdForMultipleChoice.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/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdForMultipleChoice.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the multiple choice classification loss. Indices should be in <code>[0, ..., num_choices-1]</code> where <code>num_choices</code> is the size of the second dimension of the input tensors. (See
<code>input_ids</code> above)`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L2813",returnDescription:`
<p>A <a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <em>(1,)</em>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices)</code>) \u2014 <em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Bo=new R({props:{$$slots:{default:[m0]},$$scope:{ctx:x}}}),vo=new M({props:{anchor:"transformers.BigBirdForMultipleChoice.forward.example",$$slots:{default:[g0]},$$scope:{ctx:x}}}),Ps=new q({}),Cs=new z({props:{name:"class transformers.BigBirdForTokenClassification",anchor:"transformers.BigBirdForTokenClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BigBirdForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/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/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L2896"}}),Os=new z({props:{name:"forward",anchor:"transformers.BigBirdForTokenClassification.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BigBirdForTokenClassification.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/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.BigBirdForTokenClassification.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.BigBirdForTokenClassification.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.BigBirdForTokenClassification.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.BigBirdForTokenClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BigBirdForTokenClassification.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.BigBirdForTokenClassification.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.BigBirdForTokenClassification.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.BigBirdForTokenClassification.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/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdForTokenClassification.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/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L2911",returnDescription:`
<p>A <a
  href="/docs/transformers/pr_19907/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/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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/pr_19907/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),To=new R({props:{$$slots:{default:[f0]},$$scope:{ctx:x}}}),$o=new M({props:{anchor:"transformers.BigBirdForTokenClassification.forward.example",$$slots:{default:[u0]},$$scope:{ctx:x}}}),wo=new M({props:{anchor:"transformers.BigBirdForTokenClassification.forward.example-2",$$slots:{default:[_0]},$$scope:{ctx:x}}}),Is=new q({}),Ns=new z({props:{name:"class transformers.BigBirdForQuestionAnswering",anchor:"transformers.BigBirdForQuestionAnswering",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = False"}],parametersDescription:[{anchor:"transformers.BigBirdForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/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/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L3001"}}),Ws=new z({props:{name:"forward",anchor:"transformers.BigBirdForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"question_lengths",val:" = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"start_positions",val:": typing.Optional[torch.LongTensor] = None"},{name:"end_positions",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BigBirdForQuestionAnswering.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/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.BigBirdForQuestionAnswering.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.BigBirdForQuestionAnswering.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.BigBirdForQuestionAnswering.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.BigBirdForQuestionAnswering.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BigBirdForQuestionAnswering.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.BigBirdForQuestionAnswering.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.BigBirdForQuestionAnswering.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.BigBirdForQuestionAnswering.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/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdForQuestionAnswering.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.BigBirdForQuestionAnswering.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/vr_19907/src/transformers/models/big_bird/modeling_big_bird.py#L3015",returnDescription:`
<p>A <code>transformers.models.big_bird.modeling_big_bird.BigBirdForQuestionAnsweringModelOutput</code> 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/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 1)</code>) \u2014 pooler output from BigBigModel</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.big_bird.modeling_big_bird.BigBirdForQuestionAnsweringModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Fo=new R({props:{$$slots:{default:[b0]},$$scope:{ctx:x}}}),zo=new M({props:{anchor:"transformers.BigBirdForQuestionAnswering.forward.example",$$slots:{default:[k0]},$$scope:{ctx:x}}}),qo=new M({props:{anchor:"transformers.BigBirdForQuestionAnswering.forward.example-2",$$slots:{default:[B0]},$$scope:{ctx:x}}}),Us=new q({}),Rs=new z({props:{name:"class transformers.FlaxBigBirdModel",anchor:"transformers.FlaxBigBirdModel",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": typing.Optional[tuple] = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdModel.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1830"}}),Ys=new z({props:{name:"__call__",anchor:"transformers.FlaxBigBirdModel.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.FlaxBigBirdModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxBigBirdModel.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdModel.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdModel.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxBigBirdModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1660",returnDescription:`
<p>A <a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Eo=new R({props:{$$slots:{default:[v0]},$$scope:{ctx:x}}}),jo=new M({props:{anchor:"transformers.FlaxBigBirdModel.__call__.example",$$slots:{default:[y0]},$$scope:{ctx:x}}}),Zs=new q({}),er=new z({props:{name:"class transformers.FlaxBigBirdForPreTraining",anchor:"transformers.FlaxBigBirdForPreTraining",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": typing.Optional[tuple] = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForPreTraining.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdForPreTraining.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1910"}}),lr=new z({props:{name:"__call__",anchor:"transformers.FlaxBigBirdForPreTraining.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForPreTraining.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.FlaxBigBirdForPreTraining.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxBigBirdForPreTraining.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdForPreTraining.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdForPreTraining.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxBigBirdForPreTraining.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1660",returnDescription:`
<p>A <code>transformers.models.big_bird.modeling_flax_big_bird.FlaxBigBirdForPreTrainingOutput</code> 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/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>prediction_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>seq_relationship_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, 2)</code>) \u2014 Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.big_bird.modeling_flax_big_bird.FlaxBigBirdForPreTrainingOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Co=new R({props:{$$slots:{default:[T0]},$$scope:{ctx:x}}}),Lo=new M({props:{anchor:"transformers.FlaxBigBirdForPreTraining.__call__.example",$$slots:{default:[$0]},$$scope:{ctx:x}}}),dr=new q({}),cr=new z({props:{name:"class transformers.FlaxBigBirdForCausalLM",anchor:"transformers.FlaxBigBirdForCausalLM",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": typing.Optional[tuple] = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdForCausalLM.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2546"}}),br=new z({props:{name:"__call__",anchor:"transformers.FlaxBigBirdForCausalLM.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForCausalLM.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.FlaxBigBirdForCausalLM.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxBigBirdForCausalLM.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdForCausalLM.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdForCausalLM.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxBigBirdForCausalLM.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1660",returnDescription:`
<p>A <a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions"
>transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key, value
states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting.
Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions"
>transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Oo=new R({props:{$$slots:{default:[w0]},$$scope:{ctx:x}}}),Io=new M({props:{anchor:"transformers.FlaxBigBirdForCausalLM.__call__.example",$$slots:{default:[x0]},$$scope:{ctx:x}}}),kr=new q({}),Br=new z({props:{name:"class transformers.FlaxBigBirdForMaskedLM",anchor:"transformers.FlaxBigBirdForMaskedLM",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": typing.Optional[tuple] = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdForMaskedLM.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2003"}}),qr=new z({props:{name:"__call__",anchor:"transformers.FlaxBigBirdForMaskedLM.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForMaskedLM.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.FlaxBigBirdForMaskedLM.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxBigBirdForMaskedLM.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdForMaskedLM.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdForMaskedLM.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxBigBirdForMaskedLM.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1660",returnDescription:`
<p>A <a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput"
>transformers.modeling_flax_outputs.FlaxMaskedLMOutput</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/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput"
>transformers.modeling_flax_outputs.FlaxMaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),So=new R({props:{$$slots:{default:[F0]},$$scope:{ctx:x}}}),Do=new M({props:{anchor:"transformers.FlaxBigBirdForMaskedLM.__call__.example",$$slots:{default:[z0]},$$scope:{ctx:x}}}),Mr=new q({}),Er=new z({props:{name:"class transformers.FlaxBigBirdForSequenceClassification",anchor:"transformers.FlaxBigBirdForSequenceClassification",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": typing.Optional[tuple] = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdForSequenceClassification.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2095"}}),Nr=new z({props:{name:"__call__",anchor:"transformers.FlaxBigBirdForSequenceClassification.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForSequenceClassification.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.FlaxBigBirdForSequenceClassification.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxBigBirdForSequenceClassification.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdForSequenceClassification.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdForSequenceClassification.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxBigBirdForSequenceClassification.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1660",returnDescription:`
<p>A <a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"
>transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"
>transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Uo=new R({props:{$$slots:{default:[q0]},$$scope:{ctx:x}}}),Ro=new M({props:{anchor:"transformers.FlaxBigBirdForSequenceClassification.__call__.example",$$slots:{default:[M0]},$$scope:{ctx:x}}}),Sr=new q({}),Dr=new z({props:{name:"class transformers.FlaxBigBirdForMultipleChoice",anchor:"transformers.FlaxBigBirdForMultipleChoice",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": typing.Optional[tuple] = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdForMultipleChoice.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2177"}}),Jr=new z({props:{name:"__call__",anchor:"transformers.FlaxBigBirdForMultipleChoice.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForMultipleChoice.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.FlaxBigBirdForMultipleChoice.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxBigBirdForMultipleChoice.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdForMultipleChoice.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdForMultipleChoice.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxBigBirdForMultipleChoice.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1660",returnDescription:`
<p>A <a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"
>transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, num_choices)</code>) \u2014 <em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"
>transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Qo=new R({props:{$$slots:{default:[E0]},$$scope:{ctx:x}}}),Ho=new M({props:{anchor:"transformers.FlaxBigBirdForMultipleChoice.__call__.example",$$slots:{default:[j0]},$$scope:{ctx:x}}}),Kr=new q({}),Xr=new z({props:{name:"class transformers.FlaxBigBirdForTokenClassification",anchor:"transformers.FlaxBigBirdForTokenClassification",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": typing.Optional[tuple] = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdForTokenClassification.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2276"}}),ra=new z({props:{name:"__call__",anchor:"transformers.FlaxBigBirdForTokenClassification.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForTokenClassification.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.FlaxBigBirdForTokenClassification.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxBigBirdForTokenClassification.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdForTokenClassification.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdForTokenClassification.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxBigBirdForTokenClassification.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1660",returnDescription:`
<p>A <a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"
>transformers.modeling_flax_outputs.FlaxTokenClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) \u2014 Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/pr_19907/en/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"
>transformers.modeling_flax_outputs.FlaxTokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Jo=new R({props:{$$slots:{default:[P0]},$$scope:{ctx:x}}}),Ko=new M({props:{anchor:"transformers.FlaxBigBirdForTokenClassification.__call__.example",$$slots:{default:[C0]},$$scope:{ctx:x}}}),aa=new q({}),ia=new z({props:{name:"class transformers.FlaxBigBirdForQuestionAnswering",anchor:"transformers.FlaxBigBirdForQuestionAnswering",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": typing.Optional[tuple] = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdForQuestionAnswering.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2381"}}),fa=new z({props:{name:"__call__",anchor:"transformers.FlaxBigBirdForQuestionAnswering.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"question_lengths",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForQuestionAnswering.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.FlaxBigBirdForQuestionAnswering.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxBigBirdForQuestionAnswering.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdForQuestionAnswering.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
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<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlaxBigBirdForQuestionAnswering.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2384",returnDescription:`
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<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/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>start_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-start scores (before SoftMax).</p>
</li>
<li>
<p><strong>end_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-end scores (before SoftMax).</p>
</li>
<li>
<p><strong>pooled_output</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 pooled_output returned by FlaxBigBirdModel.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.big_bird.modeling_flax_big_bird.FlaxBigBirdForQuestionAnsweringModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
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based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse
attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it
has been shown that applying sparse, global, and random attention approximates full attention, while being
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reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and
is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our
theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire
sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to
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Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon,
Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention
based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse
attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it
has been shown that applying sparse, global, and random attention approximates full attention, while being
computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context,
BigBird has shown improved performance on various long document NLP tasks, such as question answering and
summarization, compared to BERT or RoBERTa.`),ba.forEach(t),Rc=h(o),ya=s(o,"P",{});var Tc=r(ya);Wh=i(Tc,"The abstract from the paper is the following:"),Tc.forEach(t),Gc=h(o),Ta=s(o,"P",{});var $c=r(Ta);Mi=s($c,"EM",{});var wc=r(Mi);Uh=i(wc,`Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP.
Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence
length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that
reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and
is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our
theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire
sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to
8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context,
BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also
propose novel applications to genomics data.`),wc.forEach(t),$c.forEach(t),Qc=h(o),$a=s(o,"P",{});var xc=r($a);Rh=i(xc,"Tips:"),xc.forEach(t),Hc=h(o),U=s(o,"UL",{});var H=r(U);qn=s(H,"LI",{});var ka=r(qn);Gh=i(ka,"For an in-detail explanation on how BigBird\u2019s attention works, see "),Mn=s(ka,"A",{href:!0,rel:!0});var Fc=r(Mn);Qh=i(Fc,"this blog post"),Fc.forEach(t),Hh=i(ka,"."),ka.forEach(t),Vh=h(H),le=s(H,"LI",{});var ge=r(le);Jh=i(ge,"BigBird comes with 2 implementations: "),Ei=s(ge,"STRONG",{});var zc=r(Ei);Kh=i(zc,"original_full"),zc.forEach(t),Xh=i(ge," & "),ji=s(ge,"STRONG",{});var qc=r(ji);Yh=i(qc,"block_sparse"),qc.forEach(t),Zh=i(ge,`. For the sequence length < 1024, using
`),Pi=s(ge,"STRONG",{});var Mc=r(Pi);em=i(Mc,"original_full"),Mc.forEach(t),tm=i(ge," is advised as there is no benefit in using "),Ci=s(ge,"STRONG",{});var Ec=r(Ci);om=i(Ec,"block_sparse"),Ec.forEach(t),nm=i(ge," attention."),ge.forEach(t),sm=h(H),Li=s(H,"LI",{});var jc=r(Li);rm=i(jc,"The code currently uses window size of 3 blocks and 2 global blocks."),jc.forEach(t),am=h(H),Ai=s(H,"LI",{});var Pc=r(Ai);im=i(Pc,"Sequence length must be divisible by block size."),Pc.forEach(t),lm=h(H),En=s(H,"LI",{});var Ba=r(En);dm=i(Ba,"Current implementation supports only "),Oi=s(Ba,"STRONG",{});var Cc=r(Oi);cm=i(Cc,"ITC"),Cc.forEach(t),pm=i(Ba,"."),Ba.forEach(t),hm=h(H),wa=s(H,"LI",{});var ei=r(wa);mm=i(ei,"Current implementation doesn\u2019t support "),Ii=s(ei,"STRONG",{});var Lc=r(Ii);gm=i(Lc,"num_random_blocks = 0"),Lc.forEach(t),ei.forEach(t),H.forEach(t),Vc=h(o),Se=s(o,"P",{});var Wt=r(Se);fm=i(Wt,"This model was contributed by "),jn=s(Wt,"A",{href:!0,rel:!0});var Ac=r(jn);um=i(Ac,"vasudevgupta"),Ac.forEach(t),_m=i(Wt,`. The original code can be found
`),Pn=s(Wt,"A",{href:!0,rel:!0});var Oc=r(Pn);bm=i(Oc,"here"),Oc.forEach(t),km=i(Wt,"."),Wt.forEach(t),Jc=h(o),Ke=s(o,"H2",{class:!0});var va=r(Ke);Qt=s(va,"A",{id:!0,class:!0,href:!0});var Ic=r(Qt);Ni=s(Ic,"SPAN",{});var Nc=r(Ni);v(Cn.$$.fragment,Nc),Nc.forEach(t),Ic.forEach(t),Bm=h(va),Si=s(va,"SPAN",{});var Sc=r(Si);vm=i(Sc,"BigBirdConfig"),Sc.forEach(t),va.forEach(t),Kc=h(o),de=s(o,"DIV",{class:!0});var Ne=r(de);v(Ln.$$.fragment,Ne),ym=h(Ne),Xe=s(Ne,"P",{});var Ut=r(Xe);Tm=i(Ut,"This is the configuration class to store the configuration of a "),xa=s(Ut,"A",{href:!0});var Dc=r(xa);$m=i(Dc,"BigBirdModel"),Dc.forEach(t),wm=i(Ut,`. It is used to instantiate an
BigBird model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the BigBird
`),An=s(Ut,"A",{href:!0,rel:!0});var C1=r(An);xm=i(C1,"google/bigbird-roberta-base"),C1.forEach(t),Fm=i(Ut," architecture."),Ut.forEach(t),zm=h(Ne),Ye=s(Ne,"P",{});var ti=r(Ye);qm=i(ti,"Configuration objects inherit from "),Fa=s(ti,"A",{href:!0});var L1=r(Fa);Mm=i(L1,"PretrainedConfig"),L1.forEach(t),Em=i(ti,` and can be used to control the model outputs. Read the
documentation from `),za=s(ti,"A",{href:!0});var A1=r(za);jm=i(A1,"PretrainedConfig"),A1.forEach(t),Pm=i(ti," for more information."),ti.forEach(t),Cm=h(Ne),v(Ht.$$.fragment,Ne),Ne.forEach(t),Xc=h(o),Ze=s(o,"H2",{class:!0});var Ap=r(Ze);Vt=s(Ap,"A",{id:!0,class:!0,href:!0});var O1=r(Vt);Di=s(O1,"SPAN",{});var I1=r(Di);v(On.$$.fragment,I1),I1.forEach(t),O1.forEach(t),Lm=h(Ap),Wi=s(Ap,"SPAN",{});var N1=r(Wi);Am=i(N1,"BigBirdTokenizer"),N1.forEach(t),Ap.forEach(t),Yc=h(o),P=s(o,"DIV",{class:!0});var Y=r(P);v(In.$$.fragment,Y),Om=h(Y),Nn=s(Y,"P",{});var Op=r(Nn);Im=i(Op,"Construct a BigBird tokenizer. Based on "),Sn=s(Op,"A",{href:!0,rel:!0});var S1=r(Sn);Nm=i(S1,"SentencePiece"),S1.forEach(t),Sm=i(Op,"."),Op.forEach(t),Dm=h(Y),Dn=s(Y,"P",{});var Ip=r(Dn);Wm=i(Ip,"This tokenizer inherits from "),qa=s(Ip,"A",{href:!0});var D1=r(qa);Um=i(D1,"PreTrainedTokenizer"),D1.forEach(t),Rm=i(Ip,` which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.`),Ip.forEach(t),Gm=h(Y),De=s(Y,"DIV",{class:!0});var oi=r(De);v(Wn.$$.fragment,oi),Qm=h(oi),Ui=s(oi,"P",{});var W1=r(Ui);Hm=i(W1,`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A Big Bird sequence has the following format:`),W1.forEach(t),Vm=h(oi),Un=s(oi,"UL",{});var Np=r(Un);Ma=s(Np,"LI",{});var M1=r(Ma);Jm=i(M1,"single sequence: "),Ri=s(M1,"CODE",{});var U1=r(Ri);Km=i(U1,"[CLS] X [SEP]"),U1.forEach(t),M1.forEach(t),Xm=h(Np),Ea=s(Np,"LI",{});var E1=r(Ea);Ym=i(E1,"pair of sequences: "),Gi=s(E1,"CODE",{});var R1=r(Gi);Zm=i(R1,"[CLS] A [SEP] B [SEP]"),R1.forEach(t),E1.forEach(t),Np.forEach(t),oi.forEach(t),eg=h(Y),Jt=s(Y,"DIV",{class:!0});var Sp=r(Jt);v(Rn.$$.fragment,Sp),tg=h(Sp),Gn=s(Sp,"P",{});var Dp=r(Gn);og=i(Dp,`Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `),Qi=s(Dp,"CODE",{});var G1=r(Qi);ng=i(G1,"prepare_for_model"),G1.forEach(t),sg=i(Dp," method."),Dp.forEach(t),Sp.forEach(t),rg=h(Y),Kt=s(Y,"DIV",{class:!0});var Wp=r(Kt);v(Qn.$$.fragment,Wp),ag=h(Wp),et=s(Wp,"P",{});var ni=r(et);ig=i(ni,`Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
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sequence | If `),Hi=s(ni,"CODE",{});var Q1=r(Hi);lg=i(Q1,"token_ids_1"),Q1.forEach(t),dg=i(ni," is "),Vi=s(ni,"CODE",{});var H1=r(Vi);cg=i(H1,"None"),H1.forEach(t),pg=i(ni,", this method only returns the first portion of the mask (0s)."),ni.forEach(t),Wp.forEach(t),hg=h(Y),ja=s(Y,"DIV",{class:!0});var V1=r(ja);v(Hn.$$.fragment,V1),V1.forEach(t),Y.forEach(t),Zc=h(o),tt=s(o,"H2",{class:!0});var Up=r(tt);Xt=s(Up,"A",{id:!0,class:!0,href:!0});var J1=r(Xt);Ji=s(J1,"SPAN",{});var K1=r(Ji);v(Vn.$$.fragment,K1),K1.forEach(t),J1.forEach(t),mg=h(Up),Ki=s(Up,"SPAN",{});var X1=r(Ki);gg=i(X1,"BigBirdTokenizerFast"),X1.forEach(t),Up.forEach(t),ep=h(o),G=s(o,"DIV",{class:!0});var Ue=r(G);v(Jn.$$.fragment,Ue),fg=h(Ue),ze=s(Ue,"P",{});var en=r(ze);ug=i(en,"Construct a \u201Cfast\u201D BigBird tokenizer (backed by HuggingFace\u2019s "),Xi=s(en,"EM",{});var Y1=r(Xi);_g=i(Y1,"tokenizers"),Y1.forEach(t),bg=i(en,` library). Based on
`),Kn=s(en,"A",{href:!0,rel:!0});var Z1=r(Kn);kg=i(Z1,"Unigram"),Z1.forEach(t),Bg=i(en,`. This
tokenizer inherits from `),Pa=s(en,"A",{href:!0});var ey=r(Pa);vg=i(ey,"PreTrainedTokenizerFast"),ey.forEach(t),yg=i(en,` which contains most of the main methods. Users should refer to
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adding special tokens. An BigBird sequence has the following format:`),ty.forEach(t),xg=h(si),Yn=s(si,"UL",{});var Rp=r(Yn);Ca=s(Rp,"LI",{});var j1=r(Ca);Fg=i(j1,"single sequence: "),Zi=s(j1,"CODE",{});var oy=r(Zi);zg=i(oy,"[CLS] X [SEP]"),oy.forEach(t),j1.forEach(t),qg=h(Rp),La=s(Rp,"LI",{});var P1=r(La);Mg=i(P1,"pair of sequences: "),el=s(P1,"CODE",{});var ny=r(el);Eg=i(ny,"[CLS] A [SEP] B [SEP]"),ny.forEach(t),P1.forEach(t),Rp.forEach(t),si.forEach(t),jg=h(Ue),fe=s(Ue,"DIV",{class:!0});var tn=r(fe);v(Zn.$$.fragment,tn),Pg=h(tn),tl=s(tn,"P",{});var sy=r(tl);Cg=i(sy,"Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT"),sy.forEach(t),Lg=h(tn),v(Yt.$$.fragment,tn),Ag=h(tn),ol=s(tn,"P",{});var ry=r(ol);Og=i(ry,"if token_ids_1 is None, only returns the first portion of the mask (0s)."),ry.forEach(t),tn.forEach(t),Ig=h(Ue),Zt=s(Ue,"DIV",{class:!0});var Gp=r(Zt);v(es.$$.fragment,Gp),Ng=h(Gp),ts=s(Gp,"P",{});var Qp=r(ts);Sg=i(Qp,`Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `),nl=s(Qp,"CODE",{});var ay=r(nl);Dg=i(ay,"prepare_for_model"),ay.forEach(t),Wg=i(Qp," method."),Qp.forEach(t),Gp.forEach(t),Ue.forEach(t),tp=h(o),ot=s(o,"H2",{class:!0});var Hp=r(ot);eo=s(Hp,"A",{id:!0,class:!0,href:!0});var iy=r(eo);sl=s(iy,"SPAN",{});var ly=r(sl);v(os.$$.fragment,ly),ly.forEach(t),iy.forEach(t),Ug=h(Hp),rl=s(Hp,"SPAN",{});var dy=r(rl);Rg=i(dy,"BigBird specific outputs"),dy.forEach(t),Hp.forEach(t),op=h(o),nt=s(o,"DIV",{class:!0});var Vp=r(nt);v(ns.$$.fragment,Vp),Gg=h(Vp),ss=s(Vp,"P",{});var Jp=r(ss);Qg=i(Jp,"Output type of "),Aa=s(Jp,"A",{href:!0});var cy=r(Aa);Hg=i(cy,"BigBirdForPreTraining"),cy.forEach(t),Vg=i(Jp,"."),Jp.forEach(t),Vp.forEach(t),np=h(o),st=s(o,"H2",{class:!0});var Kp=r(st);to=s(Kp,"A",{id:!0,class:!0,href:!0});var py=r(to);al=s(py,"SPAN",{});var hy=r(al);v(rs.$$.fragment,hy),hy.forEach(t),py.forEach(t),Jg=h(Kp),il=s(Kp,"SPAN",{});var my=r(il);Kg=i(my,"BigBirdModel"),my.forEach(t),Kp.forEach(t),sp=h(o),Q=s(o,"DIV",{class:!0});var Re=r(Q);v(as.$$.fragment,Re),Xg=h(Re),is=s(Re,"P",{});var Xp=r(is);Yg=i(Xp,`The bare BigBird Model transformer outputting raw hidden-states without any specific head on top.
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behavior.`),Xp.forEach(t),tf=h(Re),ds=s(Re,"P",{});var Yp=r(ds);of=i(Yp,`The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
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`),pl=s(Z,"CODE",{});var ky=r(pl);gf=i(ky,"add_cross_attention"),ky.forEach(t),ff=i(Z," set to "),hl=s(Z,"CODE",{});var By=r(hl);uf=i(By,"True"),By.forEach(t),_f=i(Z,"; an "),ml=s(Z,"CODE",{});var vy=r(ml);bf=i(vy,"encoder_hidden_states"),vy.forEach(t),kf=i(Z," is then expected as an input to the forward pass."),Z.forEach(t),Bf=h(Re),ue=s(Re,"DIV",{class:!0});var on=r(ue);v(ps.$$.fragment,on),vf=h(on),rt=s(on,"P",{});var ri=r(rt);yf=i(ri,"The "),Oa=s(ri,"A",{href:!0});var yy=r(Oa);Tf=i(yy,"BigBirdModel"),yy.forEach(t),$f=i(ri," forward method, overrides the "),gl=s(ri,"CODE",{});var Ty=r(gl);wf=i(Ty,"__call__"),Ty.forEach(t),xf=i(ri," special method."),ri.forEach(t),Ff=h(on),v(oo.$$.fragment,on),zf=h(on),v(no.$$.fragment,on),on.forEach(t),Re.forEach(t),rp=h(o),at=s(o,"H2",{class:!0});var Zp=r(at);so=s(Zp,"A",{id:!0,class:!0,href:!0});var $y=r(so);fl=s($y,"SPAN",{});var wy=r(fl);v(hs.$$.fragment,wy),wy.forEach(t),$y.forEach(t),qf=h(Zp),ul=s(Zp,"SPAN",{});var xy=r(ul);Mf=i(xy,"BigBirdForPreTraining"),xy.forEach(t),Zp.forEach(t),ap=h(o),it=s(o,"DIV",{class:!0});var eh=r(it);v(ms.$$.fragment,eh),Ef=h(eh),_e=s(eh,"DIV",{class:!0});var nn=r(_e);v(gs.$$.fragment,nn),jf=h(nn),lt=s(nn,"P",{});var ai=r(lt);Pf=i(ai,"The "),Ia=s(ai,"A",{href:!0});var Fy=r(Ia);Cf=i(Fy,"BigBirdForPreTraining"),Fy.forEach(t),Lf=i(ai," forward method, overrides the "),_l=s(ai,"CODE",{});var zy=r(_l);Af=i(zy,"__call__"),zy.forEach(t),Of=i(ai," special method."),ai.forEach(t),If=h(nn),v(ro.$$.fragment,nn),Nf=h(nn),v(ao.$$.fragment,nn),nn.forEach(t),eh.forEach(t),ip=h(o),dt=s(o,"H2",{class:!0});var th=r(dt);io=s(th,"A",{id:!0,class:!0,href:!0});var qy=r(io);bl=s(qy,"SPAN",{});var My=r(bl);v(fs.$$.fragment,My),My.forEach(t),qy.forEach(t),Sf=h(th),kl=s(th,"SPAN",{});var Ey=r(kl);Df=i(Ey,"BigBirdForCausalLM"),Ey.forEach(t),th.forEach(t),lp=h(o),qe=s(o,"DIV",{class:!0});var ii=r(qe);v(us.$$.fragment,ii),Wf=h(ii),ct=s(ii,"P",{});var li=r(ct);Uf=i(li,"BigBird Model with a "),Bl=s(li,"CODE",{});var jy=r(Bl);Rf=i(jy,"language modeling"),jy.forEach(t),Gf=i(li,` head on top for CLM fine-tuning.
This model is a PyTorch `),_s=s(li,"A",{href:!0,rel:!0});var Py=r(_s);Qf=i(Py,"torch.nn.Module"),Py.forEach(t),Hf=i(li,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),li.forEach(t),Vf=h(ii),be=s(ii,"DIV",{class:!0});var sn=r(be);v(bs.$$.fragment,sn),Jf=h(sn),pt=s(sn,"P",{});var di=r(pt);Kf=i(di,"The "),Na=s(di,"A",{href:!0});var Cy=r(Na);Xf=i(Cy,"BigBirdForCausalLM"),Cy.forEach(t),Yf=i(di," forward method, overrides the "),vl=s(di,"CODE",{});var Ly=r(vl);Zf=i(Ly,"__call__"),Ly.forEach(t),eu=i(di," special method."),di.forEach(t),tu=h(sn),v(lo.$$.fragment,sn),ou=h(sn),v(co.$$.fragment,sn),sn.forEach(t),ii.forEach(t),dp=h(o),ht=s(o,"H2",{class:!0});var oh=r(ht);po=s(oh,"A",{id:!0,class:!0,href:!0});var Ay=r(po);yl=s(Ay,"SPAN",{});var Oy=r(yl);v(ks.$$.fragment,Oy),Oy.forEach(t),Ay.forEach(t),nu=h(oh),Tl=s(oh,"SPAN",{});var Iy=r(Tl);su=i(Iy,"BigBirdForMaskedLM"),Iy.forEach(t),oh.forEach(t),cp=h(o),Me=s(o,"DIV",{class:!0});var ci=r(Me);v(Bs.$$.fragment,ci),ru=h(ci),mt=s(ci,"P",{});var pi=r(mt);au=i(pi,"BigBird Model with a "),$l=s(pi,"CODE",{});var Ny=r($l);iu=i(Ny,"language modeling"),Ny.forEach(t),lu=i(pi,` head on top.
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it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),pi.forEach(t),pu=h(ci),V=s(ci,"DIV",{class:!0});var Ge=r(V);v(ys.$$.fragment,Ge),hu=h(Ge),gt=s(Ge,"P",{});var hi=r(gt);mu=i(hi,"The "),Sa=s(hi,"A",{href:!0});var Dy=r(Sa);gu=i(Dy,"BigBirdForMaskedLM"),Dy.forEach(t),fu=i(hi," forward method, overrides the "),wl=s(hi,"CODE",{});var Wy=r(wl);uu=i(Wy,"__call__"),Wy.forEach(t),_u=i(hi," special method."),hi.forEach(t),bu=h(Ge),v(ho.$$.fragment,Ge),ku=h(Ge),v(mo.$$.fragment,Ge),Bu=h(Ge),v(go.$$.fragment,Ge),Ge.forEach(t),ci.forEach(t),pp=h(o),ft=s(o,"H2",{class:!0});var nh=r(ft);fo=s(nh,"A",{id:!0,class:!0,href:!0});var Uy=r(fo);xl=s(Uy,"SPAN",{});var Ry=r(xl);v(Ts.$$.fragment,Ry),Ry.forEach(t),Uy.forEach(t),vu=h(nh),Fl=s(nh,"SPAN",{});var Gy=r(Fl);yu=i(Gy,"BigBirdForSequenceClassification"),Gy.forEach(t),nh.forEach(t),hp=h(o),ce=s(o,"DIV",{class:!0});var rn=r(ce);v($s.$$.fragment,rn),Tu=h(rn),zl=s(rn,"P",{});var Qy=r(zl);$u=i(Qy,`BigBird Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`),Qy.forEach(t),wu=h(rn),ws=s(rn,"P",{});var sh=r(ws);xu=i(sh,"This model is a PyTorch "),xs=s(sh,"A",{href:!0,rel:!0});var Hy=r(xs);Fu=i(Hy,"torch.nn.Module"),Hy.forEach(t),zu=i(sh,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),sh.forEach(t),qu=h(rn),J=s(rn,"DIV",{class:!0});var Qe=r(J);v(Fs.$$.fragment,Qe),Mu=h(Qe),ut=s(Qe,"P",{});var mi=r(ut);Eu=i(mi,"The "),Da=s(mi,"A",{href:!0});var Vy=r(Da);ju=i(Vy,"BigBirdForSequenceClassification"),Vy.forEach(t),Pu=i(mi," forward method, overrides the "),ql=s(mi,"CODE",{});var Jy=r(ql);Cu=i(Jy,"__call__"),Jy.forEach(t),Lu=i(mi," special method."),mi.forEach(t),Au=h(Qe),v(uo.$$.fragment,Qe),Ou=h(Qe),v(_o.$$.fragment,Qe),Iu=h(Qe),v(bo.$$.fragment,Qe),Qe.forEach(t),rn.forEach(t),mp=h(o),_t=s(o,"H2",{class:!0});var rh=r(_t);ko=s(rh,"A",{id:!0,class:!0,href:!0});var Ky=r(ko);Ml=s(Ky,"SPAN",{});var Xy=r(Ml);v(zs.$$.fragment,Xy),Xy.forEach(t),Ky.forEach(t),Nu=h(rh),El=s(rh,"SPAN",{});var Yy=r(El);Su=i(Yy,"BigBirdForMultipleChoice"),Yy.forEach(t),rh.forEach(t),gp=h(o),pe=s(o,"DIV",{class:!0});var an=r(pe);v(qs.$$.fragment,an),Du=h(an),jl=s(an,"P",{});var Zy=r(jl);Wu=i(Zy,`BigBird Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
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it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),ah.forEach(t),Hu=h(an),ke=s(an,"DIV",{class:!0});var ln=r(ke);v(js.$$.fragment,ln),Vu=h(ln),bt=s(ln,"P",{});var gi=r(bt);Ju=i(gi,"The "),Wa=s(gi,"A",{href:!0});var tT=r(Wa);Ku=i(tT,"BigBirdForMultipleChoice"),tT.forEach(t),Xu=i(gi," forward method, overrides the "),Pl=s(gi,"CODE",{});var oT=r(Pl);Yu=i(oT,"__call__"),oT.forEach(t),Zu=i(gi," special method."),gi.forEach(t),e_=h(ln),v(Bo.$$.fragment,ln),t_=h(ln),v(vo.$$.fragment,ln),ln.forEach(t),an.forEach(t),fp=h(o),kt=s(o,"H2",{class:!0});var ih=r(kt);yo=s(ih,"A",{id:!0,class:!0,href:!0});var nT=r(yo);Cl=s(nT,"SPAN",{});var sT=r(Cl);v(Ps.$$.fragment,sT),sT.forEach(t),nT.forEach(t),o_=h(ih),Ll=s(ih,"SPAN",{});var rT=r(Ll);n_=i(rT,"BigBirdForTokenClassification"),rT.forEach(t),ih.forEach(t),up=h(o),he=s(o,"DIV",{class:!0});var dn=r(he);v(Cs.$$.fragment,dn),s_=h(dn),Al=s(dn,"P",{});var aT=r(Al);r_=i(aT,`BigBird Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
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it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),lh.forEach(t),c_=h(dn),K=s(dn,"DIV",{class:!0});var He=r(K);v(Os.$$.fragment,He),p_=h(He),Bt=s(He,"P",{});var fi=r(Bt);h_=i(fi,"The "),Ua=s(fi,"A",{href:!0});var lT=r(Ua);m_=i(lT,"BigBirdForTokenClassification"),lT.forEach(t),g_=i(fi," forward method, overrides the "),Ol=s(fi,"CODE",{});var dT=r(Ol);f_=i(dT,"__call__"),dT.forEach(t),u_=i(fi," special method."),fi.forEach(t),__=h(He),v(To.$$.fragment,He),b_=h(He),v($o.$$.fragment,He),k_=h(He),v(wo.$$.fragment,He),He.forEach(t),dn.forEach(t),_p=h(o),vt=s(o,"H2",{class:!0});var dh=r(vt);xo=s(dh,"A",{id:!0,class:!0,href:!0});var cT=r(xo);Il=s(cT,"SPAN",{});var pT=r(Il);v(Is.$$.fragment,pT),pT.forEach(t),cT.forEach(t),B_=h(dh),Nl=s(dh,"SPAN",{});var hT=r(Nl);v_=i(hT,"BigBirdForQuestionAnswering"),hT.forEach(t),dh.forEach(t),bp=h(o),me=s(o,"DIV",{class:!0});var cn=r(me);v(Ns.$$.fragment,cn),y_=h(cn),yt=s(cn,"P",{});var ui=r(yt);T_=i(ui,`BigBird Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
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it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
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library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`),hh.forEach(t),K_=h(ee),Qs=s(ee,"P",{});var mh=r(Qs);X_=i(mh,"This model is also a Flax Linen "),Hs=s(mh,"A",{href:!0,rel:!0});var TT=r(Hs);Y_=i(TT,"flax.linen.Module"),TT.forEach(t),Z_=i(mh,`
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