import{S as Y$,i as Z$,s as ew,e as n,k as p,w as v,t as a,M as tw,c as s,d as t,m as h,a as r,x as B,h as i,b as c,G as e,g as k,y,q as T,o as $,B as w,v as ow,L as E}from"../../chunks/vendor-hf-doc-builder.js";import{T as G}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 nw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function sw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"sequence pair mask has the following format:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function rw(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function aw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function iw(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function lw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function dw(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function cw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function pw(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function hw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function mw(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(){v(d.$$.fragment)},l(g){B(d.$$.fragment,g)},m(g,u){y(d,g,u),_=!0},p:E,i(g){_||(T(d.$$.fragment,g),_=!0)},o(g){$(d.$$.fragment,g),_=!1},d(g){w(d,g)}}}function gw(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function uw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function fw(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(){v(d.$$.fragment)},l(g){B(d.$$.fragment,g)},m(g,u){y(d,g,u),_=!0},p:E,i(g){_||(T(d.$$.fragment,g),_=!0)},o(g){$(d.$$.fragment,g),_=!1},d(g){w(d,g)}}}function _w(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function bw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function kw(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function vw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function Bw(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(){v(d.$$.fragment)},l(g){B(d.$$.fragment,g)},m(g,u){y(d,g,u),_=!0},p:E,i(g){_||(T(d.$$.fragment,g),_=!0)},o(g){$(d.$$.fragment,g),_=!1},d(g){w(d,g)}}}function yw(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function Tw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function $w(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(){v(d.$$.fragment)},l(g){B(d.$$.fragment,g)},m(g,u){y(d,g,u),_=!0},p:E,i(g){_||(T(d.$$.fragment,g),_=!0)},o(g){$(d.$$.fragment,g),_=!1},d(g){w(d,g)}}}function ww(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function xw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function Fw(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function zw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function qw(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function Mw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function Ew(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function jw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function Pw(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function Cw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function Lw(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function Aw(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function Iw(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function Ow(x){let d,_,g,u,b;return u=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(),v(u.$$.fragment)},l(l){d=s(l,"P",{});var m=r(d);_=i(m,"Example:"),m.forEach(t),g=h(l),B(u.$$.fragment,l)},m(l,m){k(l,d,m),e(d,_),k(l,g,m),y(u,l,m),b=!0},p:E,i(l){b||(T(u.$$.fragment,l),b=!0)},o(l){$(u.$$.fragment,l),b=!1},d(l){l&&t(d),l&&t(g),w(u,l)}}}function Nw(x){let d,_,g,u,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"),u=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);u=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,u),e(d,b)},d(l){l&&t(d)}}}function Sw(x){let d,_,g,u,b;return u=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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Vocabulary size of the BigBird model. Defines the number of different tokens that can be represented by the
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The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
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The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 1024 or 2048 or 4096).`,name:"max_position_embeddings"},{anchor:"transformers.BigBirdConfig.type_vocab_size",description:`<strong>type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The vocabulary size of the <code>token_type_ids</code> passed when calling <a href="/docs/transformers/v4.25.1/en/model_doc/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;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.BigBirdConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#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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Will be passed to the <code>SentencePieceProcessor.__init__()</code> method. The <a href="https://github.com/google/sentencepiece/tree/master/python" rel="nofollow">Python wrapper for
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>
</ul>`,name:"sp_model_kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/tokenization_big_bird.py#L52"}}),Wn=new z({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.BigBirdTokenizer.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.BigBirdTokenizer.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
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`,returnType:`
<p><code>List[int]</code></p>
`}}),Rn=new z({props:{name:"get_special_tokens_mask",anchor:"transformers.BigBirdTokenizer.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.BigBirdTokenizer.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
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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>
`}}),Hn=new z({props:{name:"save_vocabulary",anchor:"transformers.BigBirdTokenizer.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/tokenization_big_bird.py#L246"}}),Vn=new q({}),Jn=new z({props:{name:"class transformers.BigBirdTokenizerFast",anchor:"transformers.BigBirdTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"unk_token",val:" = '<unk>'"},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{name:"pad_token",val:" = '<pad>'"},{name:"sep_token",val:" = '[SEP]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BigBirdTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.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/v4.25.1/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/v4.25.1/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/v4.25.1/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",$$slots:{default:[sw]},$$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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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/v4.25.1/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/v4.25.1/src/transformers/models/big_bird/modeling_big_bird.py#L1981",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),oo=new G({props:{$$slots:{default:[rw]},$$scope:{ctx:x}}}),no=new M({props:{anchor:"transformers.BigBirdModel.forward.example",$$slots:{default:[aw]},$$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/v4.25.1/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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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/v4.25.1/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/v4.25.1/src/transformers/models/big_bird/modeling_big_bird.py#L2282",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/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 G({props:{$$slots:{default:[iw]},$$scope:{ctx:x}}}),ao=new M({props:{anchor:"transformers.BigBirdForPreTraining.forward.example",$$slots:{default:[lw]},$$scope:{ctx:x}}}),us=new q({}),fs=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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_big_bird.py#L2514"}}),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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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/v4.25.1/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/v4.25.1/src/transformers/models/big_bird/modeling_big_bird.py#L2540",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.25.1/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/v4.25.1/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),lo=new G({props:{$$slots:{default:[dw]},$$scope:{ctx:x}}}),co=new M({props:{anchor:"transformers.BigBirdForCausalLM.forward.example",$$slots:{default:[cw]},$$scope:{ctx:x}}}),ks=new q({}),vs=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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_big_bird.py#L2370"}}),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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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/v4.25.1/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/v4.25.1/src/transformers/models/big_bird/modeling_big_bird.py#L2394",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.25.1/en/model_doc/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/v4.25.1/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ho=new G({props:{$$slots:{default:[pw]},$$scope:{ctx:x}}}),mo=new M({props:{anchor:"transformers.BigBirdForMaskedLM.forward.example",$$slots:{default:[hw]},$$scope:{ctx:x}}}),go=new M({props:{anchor:"transformers.BigBirdForMaskedLM.forward.example-2",$$slots:{default:[mw]},$$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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_big_bird.py#L2682"}}),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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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/v4.25.1/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/v4.25.1/src/transformers/models/big_bird/modeling_big_bird.py#L2693",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.25.1/en/model_doc/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/v4.25.1/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),fo=new G({props:{$$slots:{default:[gw]},$$scope:{ctx:x}}}),_o=new M({props:{anchor:"transformers.BigBirdForSequenceClassification.forward.example",$$slots:{default:[uw]},$$scope:{ctx:x}}}),bo=new M({props:{anchor:"transformers.BigBirdForSequenceClassification.forward.example-2",$$slots:{default:[fw]},$$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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_big_bird.py#L2810"}}),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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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/v4.25.1/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/v4.25.1/src/transformers/models/big_bird/modeling_big_bird.py#L2821",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.25.1/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/v4.25.1/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),vo=new G({props:{$$slots:{default:[_w]},$$scope:{ctx:x}}}),Bo=new M({props:{anchor:"transformers.BigBirdForMultipleChoice.forward.example",$$slots:{default:[bw]},$$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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_big_bird.py#L2904"}}),Is=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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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/v4.25.1/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/v4.25.1/src/transformers/models/big_bird/modeling_big_bird.py#L2919",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.25.1/en/model_doc/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/v4.25.1/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),To=new G({props:{$$slots:{default:[kw]},$$scope:{ctx:x}}}),$o=new M({props:{anchor:"transformers.BigBirdForTokenClassification.forward.example",$$slots:{default:[vw]},$$scope:{ctx:x}}}),wo=new M({props:{anchor:"transformers.BigBirdForTokenClassification.forward.example-2",$$slots:{default:[Bw]},$$scope:{ctx:x}}}),Os=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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_big_bird.py#L3009"}}),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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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/v4.25.1/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/v4.25.1/src/transformers/models/big_bird/modeling_big_bird.py#L3023",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/v4.25.1/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 G({props:{$$slots:{default:[yw]},$$scope:{ctx:x}}}),zo=new M({props:{anchor:"transformers.BigBirdForQuestionAnswering.forward.example",$$slots:{default:[Tw]},$$scope:{ctx:x}}}),qo=new M({props:{anchor:"transformers.BigBirdForQuestionAnswering.forward.example-2",$$slots:{default:[$w]},$$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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1833"}}),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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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/v4.25.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1663",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Eo=new G({props:{$$slots:{default:[ww]},$$scope:{ctx:x}}}),jo=new M({props:{anchor:"transformers.FlaxBigBirdModel.__call__.example",$$slots:{default:[xw]},$$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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1913"}}),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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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/v4.25.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1663",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/v4.25.1/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 G({props:{$$slots:{default:[Fw]},$$scope:{ctx:x}}}),Lo=new M({props:{anchor:"transformers.FlaxBigBirdForPreTraining.__call__.example",$$slots:{default:[zw]},$$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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2549"}}),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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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/v4.25.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1663",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/output#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions"
>transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Io=new G({props:{$$slots:{default:[qw]},$$scope:{ctx:x}}}),Oo=new M({props:{anchor:"transformers.FlaxBigBirdForCausalLM.__call__.example",$$slots:{default:[Mw]},$$scope:{ctx:x}}}),kr=new q({}),vr=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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2006"}}),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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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/v4.25.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1663",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput"
>transformers.modeling_flax_outputs.FlaxMaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),So=new G({props:{$$slots:{default:[Ew]},$$scope:{ctx:x}}}),Do=new M({props:{anchor:"transformers.FlaxBigBirdForMaskedLM.__call__.example",$$slots:{default:[jw]},$$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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2098"}}),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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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/v4.25.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1663",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"
>transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Uo=new G({props:{$$slots:{default:[Pw]},$$scope:{ctx:x}}}),Ro=new M({props:{anchor:"transformers.FlaxBigBirdForSequenceClassification.__call__.example",$$slots:{default:[Cw]},$$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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2180"}}),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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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/v4.25.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1663",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"
>transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Qo=new G({props:{$$slots:{default:[Lw]},$$scope:{ctx:x}}}),Ho=new M({props:{anchor:"transformers.FlaxBigBirdForMultipleChoice.__call__.example",$$slots:{default:[Aw]},$$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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2279"}}),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/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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;
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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.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/v4.25.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1663",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"
>transformers.modeling_flax_outputs.FlaxTokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Jo=new G({props:{$$slots:{default:[Iw]},$$scope:{ctx:x}}}),Ko=new M({props:{anchor:"transformers.FlaxBigBirdForTokenClassification.__call__.example",$$slots:{default:[Ow]},$$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/v4.25.1/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
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<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/v4.25.1/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
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Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a>. See <a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.25.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.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;
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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/v4.25.1/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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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
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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
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
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library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`),c1=p(),da=n("p"),p1=a("This model is also a Flax Linen "),ca=n("a"),h1=a("flax.linen.Module"),m1=a(`
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.`),g1=p(),pc=n("p"),u1=a("Finally, this model supports inherent JAX features such as:"),f1=p(),Oe=n("ul"),hc=n("li"),pa=n("a"),_1=a("Just-In-Time (JIT) compilation"),b1=p(),mc=n("li"),ha=n("a"),k1=a("Automatic Differentiation"),v1=p(),gc=n("li"),ma=n("a"),B1=a("Vectorization"),y1=p(),uc=n("li"),ga=n("a"),T1=a("Parallelization"),$1=p(),Fe=n("div"),v(ua.$$.fragment),w1=p(),Dt=n("p"),x1=a("The "),Za=n("a"),F1=a("FlaxBigBirdForQuestionAnswering"),z1=a(" forward method, overrides the "),fc=n("code"),q1=a("__call__"),M1=a(" special method."),E1=p(),v(Yo.$$.fragment),j1=p(),v(Zo.$$.fragment),this.h()},l(o){const f=tw('[data-svelte="svelte-1phssyn"]',document.head);d=s(f,"META",{name:!0,content:!0}),f.forEach(t),_=h(o),g=s(o,"H1",{class:!0});var fa=r(g);u=s(fa,"A",{id:!0,class:!0,href:!0});var _c=r(u);b=s(_c,"SPAN",{});var bc=r(b);B(l.$$.fragment,bc),bc.forEach(t),_c.forEach(t),m=h(fa),F=s(fa,"SPAN",{});var kc=r(F);Ih=i(kc,"BigBird"),kc.forEach(t),fa.forEach(t),Uc=h(o),Je=s(o,"H2",{class:!0});var _a=r(Je);Rt=s(_a,"A",{id:!0,class:!0,href:!0});var vc=r(Rt);zi=s(vc,"SPAN",{});var Bc=r(zi);B(Fn.$$.fragment,Bc),Bc.forEach(t),vc.forEach(t),Oh=h(_a),qi=s(_a,"SPAN",{});var yc=r(qi);Nh=i(yc,"Overview"),yc.forEach(t),_a.forEach(t),Rc=h(o),Gt=s(o,"P",{});var ba=r(Gt);Sh=i(ba,"The BigBird model was proposed in "),zn=s(ba,"A",{href:!0,rel:!0});var Tc=r(zn);Dh=i(Tc,"Big Bird: Transformers for Longer Sequences"),Tc.forEach(t),Wh=i(ba,` by
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),Gc=h(o),ya=s(o,"P",{});var $c=r(ya);Uh=i($c,"The abstract from the paper is the following:"),$c.forEach(t),Qc=h(o),Ta=s(o,"P",{});var wc=r(Ta);Mi=s(wc,"EM",{});var xc=r(Mi);Rh=i(xc,`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.`),xc.forEach(t),wc.forEach(t),Hc=h(o),$a=s(o,"P",{});var Fc=r($a);Gh=i(Fc,"Tips:"),Fc.forEach(t),Vc=h(o),W=s(o,"UL",{});var R=r(W);qn=s(R,"LI",{});var ka=r(qn);Qh=i(ka,"For an in-detail explanation on how BigBird\u2019s attention works, see "),Mn=s(ka,"A",{href:!0,rel:!0});var zc=r(Mn);Hh=i(zc,"this blog post"),zc.forEach(t),Vh=i(ka,"."),ka.forEach(t),Jh=h(R),le=s(R,"LI",{});var ge=r(le);Kh=i(ge,"BigBird comes with 2 implementations: "),Ei=s(ge,"STRONG",{});var qc=r(Ei);Xh=i(qc,"original_full"),qc.forEach(t),Yh=i(ge," & "),ji=s(ge,"STRONG",{});var Mc=r(ji);Zh=i(Mc,"block_sparse"),Mc.forEach(t),em=i(ge,`. For the sequence length < 1024, using
`),Pi=s(ge,"STRONG",{});var Ec=r(Pi);tm=i(Ec,"original_full"),Ec.forEach(t),om=i(ge," is advised as there is no benefit in using "),Ci=s(ge,"STRONG",{});var jc=r(Ci);nm=i(jc,"block_sparse"),jc.forEach(t),sm=i(ge," attention."),ge.forEach(t),rm=h(R),Li=s(R,"LI",{});var Pc=r(Li);am=i(Pc,"The code currently uses window size of 3 blocks and 2 global blocks."),Pc.forEach(t),im=h(R),Ai=s(R,"LI",{});var Cc=r(Ai);lm=i(Cc,"Sequence length must be divisible by block size."),Cc.forEach(t),dm=h(R),En=s(R,"LI",{});var va=r(En);cm=i(va,"Current implementation supports only "),Ii=s(va,"STRONG",{});var Lc=r(Ii);pm=i(Lc,"ITC"),Lc.forEach(t),hm=i(va,"."),va.forEach(t),mm=h(R),wa=s(R,"LI",{});var ei=r(wa);gm=i(ei,"Current implementation doesn\u2019t support "),Oi=s(ei,"STRONG",{});var Ac=r(Oi);um=i(Ac,"num_random_blocks = 0"),Ac.forEach(t),ei.forEach(t),fm=h(R),Ni=s(R,"LI",{});var Ic=r(Ni);_m=i(Ic,`BigBird is a model with absolute position embeddings so it\u2019s usually advised to pad the inputs on the right rather than
the left.`),Ic.forEach(t),R.forEach(t),Jc=h(o),Se=s(o,"P",{});var Wt=r(Se);bm=i(Wt,"This model was contributed by "),jn=s(Wt,"A",{href:!0,rel:!0});var Oc=r(jn);km=i(Oc,"vasudevgupta"),Oc.forEach(t),vm=i(Wt,`. The original code can be found
`),Pn=s(Wt,"A",{href:!0,rel:!0});var Nc=r(Pn);Bm=i(Nc,"here"),Nc.forEach(t),ym=i(Wt,"."),Wt.forEach(t),Kc=h(o),Ke=s(o,"H2",{class:!0});var Ba=r(Ke);Qt=s(Ba,"A",{id:!0,class:!0,href:!0});var Sc=r(Qt);Si=s(Sc,"SPAN",{});var Dc=r(Si);B(Cn.$$.fragment,Dc),Dc.forEach(t),Sc.forEach(t),Tm=h(Ba),Di=s(Ba,"SPAN",{});var Wc=r(Di);$m=i(Wc,"BigBirdConfig"),Wc.forEach(t),Ba.forEach(t),Xc=h(o),de=s(o,"DIV",{class:!0});var Ne=r(de);B(Ln.$$.fragment,Ne),wm=h(Ne),Xe=s(Ne,"P",{});var Ut=r(Xe);xm=i(Ut,"This is the configuration class to store the configuration of a "),xa=s(Ut,"A",{href:!0});var I1=r(xa);Fm=i(I1,"BigBirdModel"),I1.forEach(t),zm=i(Ut,`. It is used to instantiate an
BigBird model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the BigBird
`),An=s(Ut,"A",{href:!0,rel:!0});var O1=r(An);qm=i(O1,"google/bigbird-roberta-base"),O1.forEach(t),Mm=i(Ut," architecture."),Ut.forEach(t),Em=h(Ne),Ye=s(Ne,"P",{});var ti=r(Ye);jm=i(ti,"Configuration objects inherit from "),Fa=s(ti,"A",{href:!0});var N1=r(Fa);Pm=i(N1,"PretrainedConfig"),N1.forEach(t),Cm=i(ti,` and can be used to control the model outputs. Read the
documentation from `),za=s(ti,"A",{href:!0});var S1=r(za);Lm=i(S1,"PretrainedConfig"),S1.forEach(t),Am=i(ti," for more information."),ti.forEach(t),Im=h(Ne),B(Ht.$$.fragment,Ne),Ne.forEach(t),Yc=h(o),Ze=s(o,"H2",{class:!0});var Ip=r(Ze);Vt=s(Ip,"A",{id:!0,class:!0,href:!0});var D1=r(Vt);Wi=s(D1,"SPAN",{});var W1=r(Wi);B(In.$$.fragment,W1),W1.forEach(t),D1.forEach(t),Om=h(Ip),Ui=s(Ip,"SPAN",{});var U1=r(Ui);Nm=i(U1,"BigBirdTokenizer"),U1.forEach(t),Ip.forEach(t),Zc=h(o),P=s(o,"DIV",{class:!0});var Y=r(P);B(On.$$.fragment,Y),Sm=h(Y),Nn=s(Y,"P",{});var Op=r(Nn);Dm=i(Op,"Construct a BigBird tokenizer. Based on "),Sn=s(Op,"A",{href:!0,rel:!0});var R1=r(Sn);Wm=i(R1,"SentencePiece"),R1.forEach(t),Um=i(Op,"."),Op.forEach(t),Rm=h(Y),Dn=s(Y,"P",{});var Np=r(Dn);Gm=i(Np,"This tokenizer inherits from "),qa=s(Np,"A",{href:!0});var G1=r(qa);Qm=i(G1,"PreTrainedTokenizer"),G1.forEach(t),Hm=i(Np,` which contains most of the main methods. Users should refer to
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special tokens using the tokenizer `),Hi=s(Wp,"CODE",{});var J1=r(Hi);ag=i(J1,"prepare_for_model"),J1.forEach(t),ig=i(Wp," method."),Wp.forEach(t),Dp.forEach(t),lg=h(Y),Kt=s(Y,"DIV",{class:!0});var Up=r(Kt);B(Qn.$$.fragment,Up),dg=h(Up),et=s(Up,"P",{});var ni=r(et);cg=i(ni,`Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
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sequence | If `),Vi=s(ni,"CODE",{});var K1=r(Vi);pg=i(K1,"token_ids_1"),K1.forEach(t),hg=i(ni," is "),Ji=s(ni,"CODE",{});var X1=r(Ji);mg=i(X1,"None"),X1.forEach(t),gg=i(ni,", this method only returns the first portion of the mask (0s)."),ni.forEach(t),Up.forEach(t),ug=h(Y),ja=s(Y,"DIV",{class:!0});var Y1=r(ja);B(Hn.$$.fragment,Y1),Y1.forEach(t),Y.forEach(t),ep=h(o),tt=s(o,"H2",{class:!0});var Rp=r(tt);Xt=s(Rp,"A",{id:!0,class:!0,href:!0});var Z1=r(Xt);Ki=s(Z1,"SPAN",{});var ey=r(Ki);B(Vn.$$.fragment,ey),ey.forEach(t),Z1.forEach(t),fg=h(Rp),Xi=s(Rp,"SPAN",{});var ty=r(Xi);_g=i(ty,"BigBirdTokenizerFast"),ty.forEach(t),Rp.forEach(t),tp=h(o),Q=s(o,"DIV",{class:!0});var Ue=r(Q);B(Jn.$$.fragment,Ue),bg=h(Ue),ze=s(Ue,"P",{});var en=r(ze);kg=i(en,"Construct a \u201Cfast\u201D BigBird tokenizer (backed by HuggingFace\u2019s "),Yi=s(en,"EM",{});var oy=r(Yi);vg=i(oy,"tokenizers"),oy.forEach(t),Bg=i(en,` library). Based on
`),Kn=s(en,"A",{href:!0,rel:!0});var ny=r(Kn);yg=i(ny,"Unigram"),ny.forEach(t),Tg=i(en,`. This
tokenizer inherits from `),Pa=s(en,"A",{href:!0});var sy=r(Pa);$g=i(sy,"PreTrainedTokenizerFast"),sy.forEach(t),wg=i(en,` which contains most of the main methods. Users should refer to
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special tokens using the tokenizer `),sl=s(Hp,"CODE",{});var cy=r(sl);Rg=i(cy,"prepare_for_model"),cy.forEach(t),Gg=i(Hp," method."),Hp.forEach(t),Qp.forEach(t),Ue.forEach(t),op=h(o),ot=s(o,"H2",{class:!0});var Vp=r(ot);eo=s(Vp,"A",{id:!0,class:!0,href:!0});var py=r(eo);rl=s(py,"SPAN",{});var hy=r(rl);B(os.$$.fragment,hy),hy.forEach(t),py.forEach(t),Qg=h(Vp),al=s(Vp,"SPAN",{});var my=r(al);Hg=i(my,"BigBird specific outputs"),my.forEach(t),Vp.forEach(t),np=h(o),nt=s(o,"DIV",{class:!0});var Jp=r(nt);B(ns.$$.fragment,Jp),Vg=h(Jp),ss=s(Jp,"P",{});var Kp=r(ss);Jg=i(Kp,"Output type of "),Aa=s(Kp,"A",{href:!0});var gy=r(Aa);Kg=i(gy,"BigBirdForPreTraining"),gy.forEach(t),Xg=i(Kp,"."),Kp.forEach(t),Jp.forEach(t),sp=h(o),st=s(o,"H2",{class:!0});var Xp=r(st);to=s(Xp,"A",{id:!0,class:!0,href:!0});var uy=r(to);il=s(uy,"SPAN",{});var fy=r(il);B(rs.$$.fragment,fy),fy.forEach(t),uy.forEach(t),Yg=h(Xp),ll=s(Xp,"SPAN",{});var _y=r(ll);Zg=i(_y,"BigBirdModel"),_y.forEach(t),Xp.forEach(t),rp=h(o),H=s(o,"DIV",{class:!0});var Re=r(H);B(as.$$.fragment,Re),eu=h(Re),is=s(Re,"P",{});var Yp=r(is);tu=i(Yp,`The bare BigBird Model transformer outputting raw hidden-states without any specific head on top.
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`),hl=s(Z,"CODE",{});var Ty=r(hl);fu=i(Ty,"add_cross_attention"),Ty.forEach(t),_u=i(Z," set to "),ml=s(Z,"CODE",{});var $y=r(ml);bu=i($y,"True"),$y.forEach(t),ku=i(Z,"; an "),gl=s(Z,"CODE",{});var wy=r(gl);vu=i(wy,"encoder_hidden_states"),wy.forEach(t),Bu=i(Z," is then expected as an input to the forward pass."),Z.forEach(t),yu=h(Re),fe=s(Re,"DIV",{class:!0});var on=r(fe);B(ps.$$.fragment,on),Tu=h(on),rt=s(on,"P",{});var ri=r(rt);$u=i(ri,"The "),Ia=s(ri,"A",{href:!0});var xy=r(Ia);wu=i(xy,"BigBirdModel"),xy.forEach(t),xu=i(ri," forward method, overrides the "),ul=s(ri,"CODE",{});var Fy=r(ul);Fu=i(Fy,"__call__"),Fy.forEach(t),zu=i(ri," special method."),ri.forEach(t),qu=h(on),B(oo.$$.fragment,on),Mu=h(on),B(no.$$.fragment,on),on.forEach(t),Re.forEach(t),ap=h(o),at=s(o,"H2",{class:!0});var eh=r(at);so=s(eh,"A",{id:!0,class:!0,href:!0});var zy=r(so);fl=s(zy,"SPAN",{});var qy=r(fl);B(hs.$$.fragment,qy),qy.forEach(t),zy.forEach(t),Eu=h(eh),_l=s(eh,"SPAN",{});var My=r(_l);ju=i(My,"BigBirdForPreTraining"),My.forEach(t),eh.forEach(t),ip=h(o),it=s(o,"DIV",{class:!0});var th=r(it);B(ms.$$.fragment,th),Pu=h(th),_e=s(th,"DIV",{class:!0});var nn=r(_e);B(gs.$$.fragment,nn),Cu=h(nn),lt=s(nn,"P",{});var ai=r(lt);Lu=i(ai,"The "),Oa=s(ai,"A",{href:!0});var Ey=r(Oa);Au=i(Ey,"BigBirdForPreTraining"),Ey.forEach(t),Iu=i(ai," forward method, overrides the "),bl=s(ai,"CODE",{});var jy=r(bl);Ou=i(jy,"__call__"),jy.forEach(t),Nu=i(ai," special method."),ai.forEach(t),Su=h(nn),B(ro.$$.fragment,nn),Du=h(nn),B(ao.$$.fragment,nn),nn.forEach(t),th.forEach(t),lp=h(o),dt=s(o,"H2",{class:!0});var oh=r(dt);io=s(oh,"A",{id:!0,class:!0,href:!0});var Py=r(io);kl=s(Py,"SPAN",{});var Cy=r(kl);B(us.$$.fragment,Cy),Cy.forEach(t),Py.forEach(t),Wu=h(oh),vl=s(oh,"SPAN",{});var Ly=r(vl);Uu=i(Ly,"BigBirdForCausalLM"),Ly.forEach(t),oh.forEach(t),dp=h(o),qe=s(o,"DIV",{class:!0});var ii=r(qe);B(fs.$$.fragment,ii),Ru=h(ii),ct=s(ii,"P",{});var li=r(ct);Gu=i(li,"BigBird Model with a "),Bl=s(li,"CODE",{});var Ay=r(Bl);Qu=i(Ay,"language modeling"),Ay.forEach(t),Hu=i(li,` head on top for CLM fine-tuning.
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This model is a PyTorch `),Bs=s(pi,"A",{href:!0,rel:!0});var Ry=r(Bs);hf=i(Ry,"torch.nn.Module"),Ry.forEach(t),mf=i(pi,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),pi.forEach(t),gf=h(ci),V=s(ci,"DIV",{class:!0});var Ge=r(V);B(ys.$$.fragment,Ge),uf=h(Ge),gt=s(Ge,"P",{});var hi=r(gt);ff=i(hi,"The "),Sa=s(hi,"A",{href:!0});var Gy=r(Sa);_f=i(Gy,"BigBirdForMaskedLM"),Gy.forEach(t),bf=i(hi," forward method, overrides the "),xl=s(hi,"CODE",{});var Qy=r(xl);kf=i(Qy,"__call__"),Qy.forEach(t),vf=i(hi," special method."),hi.forEach(t),Bf=h(Ge),B(ho.$$.fragment,Ge),yf=h(Ge),B(mo.$$.fragment,Ge),Tf=h(Ge),B(go.$$.fragment,Ge),Ge.forEach(t),ci.forEach(t),hp=h(o),ut=s(o,"H2",{class:!0});var sh=r(ut);uo=s(sh,"A",{id:!0,class:!0,href:!0});var Hy=r(uo);Fl=s(Hy,"SPAN",{});var Vy=r(Fl);B(Ts.$$.fragment,Vy),Vy.forEach(t),Hy.forEach(t),$f=h(sh),zl=s(sh,"SPAN",{});var Jy=r(zl);wf=i(Jy,"BigBirdForSequenceClassification"),Jy.forEach(t),sh.forEach(t),mp=h(o),ce=s(o,"DIV",{class:!0});var rn=r(ce);B($s.$$.fragment,rn),xf=h(rn),ql=s(rn,"P",{});var Ky=r(ql);Ff=i(Ky,`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.`),Ky.forEach(t),zf=h(rn),ws=s(rn,"P",{});var rh=r(ws);qf=i(rh,"This model is a PyTorch "),xs=s(rh,"A",{href:!0,rel:!0});var Xy=r(xs);Mf=i(Xy,"torch.nn.Module"),Xy.forEach(t),Ef=i(rh,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),rh.forEach(t),jf=h(rn),J=s(rn,"DIV",{class:!0});var Qe=r(J);B(Fs.$$.fragment,Qe),Pf=h(Qe),ft=s(Qe,"P",{});var mi=r(ft);Cf=i(mi,"The "),Da=s(mi,"A",{href:!0});var Yy=r(Da);Lf=i(Yy,"BigBirdForSequenceClassification"),Yy.forEach(t),Af=i(mi," forward method, overrides the "),Ml=s(mi,"CODE",{});var Zy=r(Ml);If=i(Zy,"__call__"),Zy.forEach(t),Of=i(mi," special method."),mi.forEach(t),Nf=h(Qe),B(fo.$$.fragment,Qe),Sf=h(Qe),B(_o.$$.fragment,Qe),Df=h(Qe),B(bo.$$.fragment,Qe),Qe.forEach(t),rn.forEach(t),gp=h(o),_t=s(o,"H2",{class:!0});var ah=r(_t);ko=s(ah,"A",{id:!0,class:!0,href:!0});var e2=r(ko);El=s(e2,"SPAN",{});var t2=r(El);B(zs.$$.fragment,t2),t2.forEach(t),e2.forEach(t),Wf=h(ah),jl=s(ah,"SPAN",{});var o2=r(jl);Uf=i(o2,"BigBirdForMultipleChoice"),o2.forEach(t),ah.forEach(t),up=h(o),pe=s(o,"DIV",{class:!0});var an=r(pe);B(qs.$$.fragment,an),Rf=h(an),Pl=s(an,"P",{});var n2=r(Pl);Gf=i(n2,`BigBird Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.`),n2.forEach(t),Qf=h(an),Ms=s(an,"P",{});var ih=r(Ms);Hf=i(ih,"This model is a PyTorch "),Es=s(ih,"A",{href:!0,rel:!0});var s2=r(Es);Vf=i(s2,"torch.nn.Module"),s2.forEach(t),Jf=i(ih,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),ih.forEach(t),Kf=h(an),ke=s(an,"DIV",{class:!0});var ln=r(ke);B(js.$$.fragment,ln),Xf=h(ln),bt=s(ln,"P",{});var gi=r(bt);Yf=i(gi,"The "),Wa=s(gi,"A",{href:!0});var r2=r(Wa);Zf=i(r2,"BigBirdForMultipleChoice"),r2.forEach(t),e_=i(gi," forward method, overrides the "),Cl=s(gi,"CODE",{});var a2=r(Cl);t_=i(a2,"__call__"),a2.forEach(t),o_=i(gi," special method."),gi.forEach(t),n_=h(ln),B(vo.$$.fragment,ln),s_=h(ln),B(Bo.$$.fragment,ln),ln.forEach(t),an.forEach(t),fp=h(o),kt=s(o,"H2",{class:!0});var lh=r(kt);yo=s(lh,"A",{id:!0,class:!0,href:!0});var i2=r(yo);Ll=s(i2,"SPAN",{});var l2=r(Ll);B(Ps.$$.fragment,l2),l2.forEach(t),i2.forEach(t),r_=h(lh),Al=s(lh,"SPAN",{});var d2=r(Al);a_=i(d2,"BigBirdForTokenClassification"),d2.forEach(t),lh.forEach(t),_p=h(o),he=s(o,"DIV",{class:!0});var dn=r(he);B(Cs.$$.fragment,dn),i_=h(dn),Il=s(dn,"P",{});var c2=r(Il);l_=i(c2,`BigBird Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
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behavior.`),dh.forEach(t),m_=h(dn),K=s(dn,"DIV",{class:!0});var He=r(K);B(Is.$$.fragment,He),g_=h(He),vt=s(He,"P",{});var ui=r(vt);u_=i(ui,"The "),Ua=s(ui,"A",{href:!0});var h2=r(Ua);f_=i(h2,"BigBirdForTokenClassification"),h2.forEach(t),__=i(ui," forward method, overrides the "),Ol=s(ui,"CODE",{});var m2=r(Ol);b_=i(m2,"__call__"),m2.forEach(t),k_=i(ui," special method."),ui.forEach(t),v_=h(He),B(To.$$.fragment,He),B_=h(He),B($o.$$.fragment,He),y_=h(He),B(wo.$$.fragment,He),He.forEach(t),dn.forEach(t),bp=h(o),Bt=s(o,"H2",{class:!0});var ch=r(Bt);xo=s(ch,"A",{id:!0,class:!0,href:!0});var g2=r(xo);Nl=s(g2,"SPAN",{});var u2=r(Nl);B(Os.$$.fragment,u2),u2.forEach(t),g2.forEach(t),T_=h(ch),Sl=s(ch,"SPAN",{});var f2=r(Sl);$_=i(f2,"BigBirdForQuestionAnswering"),f2.forEach(t),ch.forEach(t),kp=h(o),me=s(o,"DIV",{class:!0});var cn=r(me);B(Ns.$$.fragment,cn),w_=h(cn),yt=s(cn,"P",{});var fi=r(yt);x_=i(fi,`BigBird Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
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it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),ph.forEach(t),L_=h(cn),X=s(cn,"DIV",{class:!0});var Ve=r(X);B(Ws.$$.fragment,Ve),A_=h(Ve),Tt=s(Ve,"P",{});var _i=r(Tt);I_=i(_i,"The "),Ra=s(_i,"A",{href:!0});var v2=r(Ra);O_=i(v2,"BigBirdForQuestionAnswering"),v2.forEach(t),N_=i(_i," forward method, overrides the "),Ul=s(_i,"CODE",{});var B2=r(Ul);S_=i(B2,"__call__"),B2.forEach(t),D_=i(_i," special method."),_i.forEach(t),W_=h(Ve),B(Fo.$$.fragment,Ve),U_=h(Ve),B(zo.$$.fragment,Ve),R_=h(Ve),B(qo.$$.fragment,Ve),Ve.forEach(t),cn.forEach(t),vp=h(o),$t=s(o,"H2",{class:!0});var hh=r($t);Mo=s(hh,"A",{id:!0,class:!0,href:!0});var y2=r(Mo);Rl=s(y2,"SPAN",{});var T2=r(Rl);B(Us.$$.fragment,T2),T2.forEach(t),y2.forEach(t),G_=h(hh),Gl=s(hh,"SPAN",{});var $2=r(Gl);Q_=i($2,"FlaxBigBirdModel"),$2.forEach(t),hh.forEach(t),Bp=h(o),C=s(o,"DIV",{class:!0});var ee=r(C);B(Rs.$$.fragment,ee),H_=h(ee),Ql=s(ee,"P",{});var w2=r(Ql);V_=i(w2,"The bare BigBird Model transformer outputting raw hidden-states without any specific head on top."),w2.forEach(t),J_=h(ee),Gs=s(ee,"P",{});var mh=r(Gs);K_=i(mh,"This model inherits from "),Ga=s(mh,"A",{href:!0});var x2=r(Ga);X_=i(x2,"FlaxPreTrainedModel"),x2.forEach(t),Y_=i(mh,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`),mh.forEach(t),Z_=h(ee),Qs=s(ee,"P",{});var gh=r(Qs);eb=i(gh,"This model is also a Flax Linen "),Hs=s(gh,"A",{href:!0,rel:!0});var F2=r(Hs);tb=i(F2,"flax.linen.Module"),F2.forEach(t),ob=i(gh,`
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library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`),fh.forEach(t),Cb=h(te),or=s(te,"P",{});var _h=r(or);Lb=i(_h,"This model is also a Flax Linen "),nr=s(_h,"A",{href:!0,rel:!0});var G2=r(nr);Ab=i(G2,"flax.linen.Module"),G2.forEach(t),Ib=i(_h,`
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.`),_h.forEach(t),Ob=h(te),sd=s(te,"P",{});var Q2=r(sd);Nb=i(Q2,"Finally, this model supports inherent JAX features such as:"),Q2.forEach(t),Sb=h(te),je=s(te,"UL",{});var mn=r(je);rd=s(mn,"LI",{});var H2=r(rd);sr=s(H2,"A",{href:!0,rel:!0});var V2=r(sr);Db=i(V2,"Just-In-Time (JIT) compilation"),V2.forEach(t),H2.forEach(t),Wb=h(mn),ad=s(mn,"LI",{});var J2=r(ad);rr=s(J2,"A",{href:!0,rel:!0});var K2=r(rr);Ub=i(K2,"Automatic Differentiation"),K2.forEach(t),J2.forEach(t),Rb=h(mn),id=s(mn,"LI",{});var X2=r(id);ar=s(X2,"A",{href:!0,rel:!0});var Y2=r(ar);Gb=i(Y2,"Vectorization"),Y2.forEach(t),X2.forEach(t),Qb=h(mn),ld=s(mn,"LI",{});var Z2=r(ld);ir=s(Z2,"A",{href:!0,rel:!0});var eT=r(ir);Hb=i(eT,"Parallelization"),eT.forEach(t),Z2.forEach(t),mn.forEach(t),Vb=h(te),Be=s(te,"DIV",{class:!0});var gn=r(Be);B(lr.$$.fragment,gn),Jb=h(gn),zt=s(gn,"P",{});var vi=r(zt);Kb=i(vi,"The "),dd=s(vi,"CODE",{});var tT=r(dd);Xb=i(tT,"FlaxBigBirdPreTrainedModel"),tT.forEach(t),Yb=i(vi," forward method, overrides the "),cd=s(vi,"CODE",{});var oT=r(cd);Zb=i(oT,"__call__"),oT.forEach(t),ek=i(vi," special method."),vi.forEach(t),tk=h(gn),B(Co.$$.fragment,gn),ok=h(gn),B(Lo.$$.fragment,gn),gn.forEach(t),te.forEach(t),$p=h(o),qt=s(o,"H2",{class:!0});var bh=r(qt);Ao=s(bh,"A",{id:!0,class:!0,href:!0});var nT=r(Ao);pd=s(nT,"SPAN",{});var sT=r(pd);B(dr.$$.fragment,sT),sT.forEach(t),nT.forEach(t),nk=h(bh),hd=s(bh,"SPAN",{});var rT=r(hd);sk=i(rT,"FlaxBigBirdForCausalLM"),rT.forEach(t),bh.forEach(t),wp=h(o),A=s(o,"DIV",{class:!0});var oe=r(A);B(cr.$$.fragment,oe),rk=h(oe),md=s(oe,"P",{});var aT=r(md);ak=i(aT,`BigBird Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for
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library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`),kh.forEach(t),pk=h(oe),hr=s(oe,"P",{});var vh=r(hr);hk=i(vh,"This model is also a Flax Linen "),mr=s(vh,"A",{href:!0,rel:!0});var lT=r(mr);mk=i(lT,"flax.linen.Module"),lT.forEach(t),gk=i(vh,`
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
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library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`),Th.forEach(t),Rk=h(ne),Tr=s(ne,"P",{});var $h=r(Tr);Gk=i($h,"This model is also a Flax Linen "),$r=s($h,"A",{href:!0,rel:!0});var wT=r($r);Qk=i(wT,"flax.linen.Module"),wT.forEach(t),Hk=i($h,`
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outputs"},{local:"transformers.BigBirdModel",title:"BigBirdModel"},{local:"transformers.BigBirdForPreTraining",title:"BigBirdForPreTraining"},{local:"transformers.BigBirdForCausalLM",title:"BigBirdForCausalLM"},{local:"transformers.BigBirdForMaskedLM",title:"BigBirdForMaskedLM"},{local:"transformers.BigBirdForSequenceClassification",title:"BigBirdForSequenceClassification"},{local:"transformers.BigBirdForMultipleChoice",title:"BigBirdForMultipleChoice"},{local:"transformers.BigBirdForTokenClassification",title:"BigBirdForTokenClassification"},{local:"transformers.BigBirdForQuestionAnswering",title:"BigBirdForQuestionAnswering"},{local:"transformers.FlaxBigBirdModel",title:"FlaxBigBirdModel"},{local:"transformers.FlaxBigBirdForPreTraining",title:"FlaxBigBirdForPreTraining"},{local:"transformers.FlaxBigBirdForCausalLM",title:"FlaxBigBirdForCausalLM"},{local:"transformers.FlaxBigBirdForMaskedLM",title:"FlaxBigBirdForMaskedLM"},{local:"transformers.FlaxBigBirdForSequenceClassification",title:"FlaxBigBirdForSequenceClassification"},{local:"transformers.FlaxBigBirdForMultipleChoice",title:"FlaxBigBirdForMultipleChoice"},{local:"transformers.FlaxBigBirdForTokenClassification",title:"FlaxBigBirdForTokenClassification"},{local:"transformers.FlaxBigBirdForQuestionAnswering",title:"FlaxBigBirdForQuestionAnswering"}],title:"BigBird"};function Uw(x){return ow(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Kw extends Y${constructor(d){super();Z$(this,d,Uw,Dw,ew,{})}}export{Kw as default,Ww as metadata};
