import{S as Gc,i as Yc,s as Xc,e as o,k as p,w as v,t as i,M as Kc,c as s,d as t,m as h,a as r,x as w,h as l,b as f,G as e,g as _,y as $,q as C,o as y,B as T,v as Zc,L as J}from"../../chunks/vendor-hf-doc-builder.js";import{T as Yo}from"../../chunks/Tip-hf-doc-builder.js";import{D as F}from"../../chunks/Docstring-hf-doc-builder.js";import{C as O}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Y}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as V}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function ed(q){let c,k,d,m,b;return m=new O({props:{code:`from transformers import CanineConfig, CanineModel

# Initializing a CANINE google/canine-s style configuration
configuration = CanineConfig()

# Initializing a model (with random weights) from the google/canine-s style configuration
model = CanineModel(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> CanineConfig, CanineModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CANINE google/canine-s style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = CanineConfig()

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

tokenizer = CanineTokenizer.from_pretrained("google/canine-s")
model = CanineModel.from_pretrained("google/canine-s")

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

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

tokenizer = CanineTokenizer.from_pretrained("vicl/canine-c-finetuned-cola")
model = CanineForSequenceClassification.from_pretrained("vicl/canine-c-finetuned-cola")

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

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

predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CanineTokenizer, CanineForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = CanineTokenizer.from_pretrained(<span class="hljs-string">&quot;vicl/canine-c-finetuned-cola&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CanineForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;vicl/canine-c-finetuned-cola&quot;</span>)

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_id = logits.argmax().item()
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.id2label[predicted_class_id]
<span class="hljs-string">&#x27;LABEL_0&#x27;</span>`}}),{c(){c=o("p"),k=i("Example of single-label classification:"),d=p(),v(m.$$.fragment)},l(a){c=s(a,"P",{});var g=r(c);k=l(g,"Example of single-label classification:"),g.forEach(t),d=h(a),w(m.$$.fragment,a)},m(a,g){_(a,c,g),e(c,k),_(a,d,g),$(m,a,g),b=!0},p:J,i(a){b||(C(m.$$.fragment,a),b=!0)},o(a){y(m.$$.fragment,a),b=!1},d(a){a&&t(c),a&&t(d),T(m,a)}}}function rd(q){let c,k;return c=new O({props:{code:'# To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`\nnum_labels = len(model.config.id2label)\nmodel = CanineForSequenceClassification.from_pretrained("vicl/canine-c-finetuned-cola", num_labels=num_labels)\n\nlabels = torch.tensor([1])\nloss = model(**inputs, labels=labels).loss\nround(loss.item(), 2)',highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># To train a model on \`num_labels\` classes, you can pass \`num_labels=num_labels\` to \`.from_pretrained(...)\`</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>num_labels = <span class="hljs-built_in">len</span>(model.config.id2label)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CanineForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;vicl/canine-c-finetuned-cola&quot;</span>, num_labels=num_labels)

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = torch.tensor([<span class="hljs-number">1</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(loss.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">0.82</span>`}}),{c(){v(c.$$.fragment)},l(d){w(c.$$.fragment,d)},m(d,m){$(c,d,m),k=!0},p:J,i(d){k||(C(c.$$.fragment,d),k=!0)},o(d){y(c.$$.fragment,d),k=!1},d(d){T(c,d)}}}function id(q){let c,k,d,m,b;return m=new O({props:{code:`import torch
from transformers import CanineTokenizer, CanineForSequenceClassification

tokenizer = CanineTokenizer.from_pretrained("vicl/canine-c-finetuned-cola")
model = CanineForSequenceClassification.from_pretrained("vicl/canine-c-finetuned-cola", problem_type="multi_label_classification")

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

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

predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CanineTokenizer, CanineForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = CanineTokenizer.from_pretrained(<span class="hljs-string">&quot;vicl/canine-c-finetuned-cola&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CanineForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;vicl/canine-c-finetuned-cola&quot;</span>, problem_type=<span class="hljs-string">&quot;multi_label_classification&quot;</span>)

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_id = logits.argmax().item()
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.id2label[predicted_class_id]
<span class="hljs-string">&#x27;LABEL_0&#x27;</span>`}}),{c(){c=o("p"),k=i("Example of multi-label classification:"),d=p(),v(m.$$.fragment)},l(a){c=s(a,"P",{});var g=r(c);k=l(g,"Example of multi-label classification:"),g.forEach(t),d=h(a),w(m.$$.fragment,a)},m(a,g){_(a,c,g),e(c,k),_(a,d,g),$(m,a,g),b=!0},p:J,i(a){b||(C(m.$$.fragment,a),b=!0)},o(a){y(m.$$.fragment,a),b=!1},d(a){a&&t(c),a&&t(d),T(m,a)}}}function ld(q){let c,k;return c=new O({props:{code:`# To train a model on \`num_labels\` classes, you can pass \`num_labels=num_labels\` to \`.from_pretrained(...)\`
num_labels = len(model.config.id2label)
model = CanineForSequenceClassification.from_pretrained(
    "vicl/canine-c-finetuned-cola", num_labels=num_labels, problem_type="multi_label_classification"
)

labels = torch.nn.functional.one_hot(torch.tensor([predicted_class_id]), num_classes=num_labels).to(
    torch.float
)
loss = model(**inputs, labels=labels).loss
loss.backward()`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># To train a model on \`num_labels\` classes, you can pass \`num_labels=num_labels\` to \`.from_pretrained(...)\`</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>num_labels = <span class="hljs-built_in">len</span>(model.config.id2label)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CanineForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;vicl/canine-c-finetuned-cola&quot;</span>, num_labels=num_labels, problem_type=<span class="hljs-string">&quot;multi_label_classification&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = torch.nn.functional.one_hot(torch.tensor([predicted_class_id]), num_classes=num_labels).to(
<span class="hljs-meta">... </span>    torch.<span class="hljs-built_in">float</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss
<span class="hljs-meta">&gt;&gt;&gt; </span>loss.backward()`}}),{c(){v(c.$$.fragment)},l(d){w(c.$$.fragment,d)},m(d,m){$(c,d,m),k=!0},p:J,i(d){k||(C(c.$$.fragment,d),k=!0)},o(d){y(c.$$.fragment,d),k=!1},d(d){T(c,d)}}}function cd(q){let c,k,d,m,b;return{c(){c=o("p"),k=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),d=o("code"),m=i("Module"),b=i(`
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(a){c=s(a,"P",{});var g=r(c);k=l(g,"Although the recipe for forward pass needs to be defined within this function, one should call the "),d=s(g,"CODE",{});var z=r(d);m=l(z,"Module"),z.forEach(t),b=l(g,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),g.forEach(t)},m(a,g){_(a,c,g),e(c,k),e(c,d),e(d,m),e(c,b)},d(a){a&&t(c)}}}function dd(q){let c,k,d,m,b;return m=new O({props:{code:`from transformers import CanineTokenizer, CanineForMultipleChoice
import torch

tokenizer = CanineTokenizer.from_pretrained("google/canine-s")
model = CanineForMultipleChoice.from_pretrained("google/canine-s")

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

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

tokenizer = CanineTokenizer.from_pretrained("google/canine-s")
model = CanineForTokenClassification.from_pretrained("google/canine-s")

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = CanineTokenizer.from_pretrained(<span class="hljs-string">&quot;google/canine-s&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CanineForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google/canine-s&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`}}),{c(){c=o("p"),k=i("Example:"),d=p(),v(m.$$.fragment)},l(a){c=s(a,"P",{});var g=r(c);k=l(g,"Example:"),g.forEach(t),d=h(a),w(m.$$.fragment,a)},m(a,g){_(a,c,g),e(c,k),_(a,d,g),$(m,a,g),b=!0},p:J,i(a){b||(C(m.$$.fragment,a),b=!0)},o(a){y(m.$$.fragment,a),b=!1},d(a){a&&t(c),a&&t(d),T(m,a)}}}function md(q){let c,k;return c=new O({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>)`}}),{c(){v(c.$$.fragment)},l(d){w(c.$$.fragment,d)},m(d,m){$(c,d,m),k=!0},p:J,i(d){k||(C(c.$$.fragment,d),k=!0)},o(d){y(c.$$.fragment,d),k=!1},d(d){T(c,d)}}}function fd(q){let c,k,d,m,b;return{c(){c=o("p"),k=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),d=o("code"),m=i("Module"),b=i(`
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(a){c=s(a,"P",{});var g=r(c);k=l(g,"Although the recipe for forward pass needs to be defined within this function, one should call the "),d=s(g,"CODE",{});var z=r(d);m=l(z,"Module"),z.forEach(t),b=l(g,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),g.forEach(t)},m(a,g){_(a,c,g),e(c,k),e(c,d),e(d,m),e(c,b)},d(a){a&&t(c)}}}function ud(q){let c,k,d,m,b;return m=new O({props:{code:`from transformers import CanineTokenizer, CanineForQuestionAnswering
import torch

tokenizer = CanineTokenizer.from_pretrained("Splend1dchan/canine-c-squad")
model = CanineForQuestionAnswering.from_pretrained("Splend1dchan/canine-c-squad")

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

inputs = tokenizer(question, text, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

answer_start_index = outputs.start_logits.argmax()
answer_end_index = outputs.end_logits.argmax()

predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CanineTokenizer, CanineForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = CanineTokenizer.from_pretrained(<span class="hljs-string">&quot;Splend1dchan/canine-c-squad&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CanineForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;Splend1dchan/canine-c-squad&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>question, text = <span class="hljs-string">&quot;Who was Jim Henson?&quot;</span>, <span class="hljs-string">&quot;Jim Henson was a nice puppet&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(question, text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>answer_start_index = outputs.start_logits.argmax()
<span class="hljs-meta">&gt;&gt;&gt; </span>answer_end_index = outputs.end_logits.argmax()

<span class="hljs-meta">&gt;&gt;&gt; </span>predict_answer_tokens = inputs.input_ids[<span class="hljs-number">0</span>, answer_start_index : answer_end_index + <span class="hljs-number">1</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(predict_answer_tokens, skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-string">&#x27;nice puppet&#x27;</span>`}}),{c(){c=o("p"),k=i("Example:"),d=p(),v(m.$$.fragment)},l(a){c=s(a,"P",{});var g=r(c);k=l(g,"Example:"),g.forEach(t),d=h(a),w(m.$$.fragment,a)},m(a,g){_(a,c,g),e(c,k),_(a,d,g),$(m,a,g),b=!0},p:J,i(a){b||(C(m.$$.fragment,a),b=!0)},o(a){y(m.$$.fragment,a),b=!1},d(a){a&&t(c),a&&t(d),T(m,a)}}}function gd(q){let c,k;return c=new O({props:{code:`# target is "nice puppet"
target_start_index = torch.tensor([14])
target_end_index = torch.tensor([15])

outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
loss = outputs.loss
round(loss.item(), 2)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># target is &quot;nice puppet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>target_start_index = torch.tensor([<span class="hljs-number">14</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>target_end_index = torch.tensor([<span class="hljs-number">15</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(loss.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">8.81</span>`}}),{c(){v(c.$$.fragment)},l(d){w(c.$$.fragment,d)},m(d,m){$(c,d,m),k=!0},p:J,i(d){k||(C(c.$$.fragment,d),k=!0)},o(d){y(c.$$.fragment,d),k=!1},d(d){T(c,d)}}}function _d(q){let c,k,d,m,b,a,g,z,Rs,Xo,te,$e,Lt,rn,Vs,Dt,Js,Ko,Ce,Gs,ln,Ys,Xs,Zo,st,Ks,es,at,Wt,Zs,ns,rt,ea,ts,j,Bt,Qt,na,ta,Ht,cn,oa,it,sa,aa,ra,Ut,Rt,ia,la,Vt,Jt,ca,da,Gt,lt,dn,pa,ha,ma,Yt,ct,pn,fa,ua,os,X,ga,hn,_a,ka,mn,ba,va,ss,oe,ye,Xt,fn,wa,Kt,$a,as,dt,Ca,rs,un,is,pt,ya,ls,gn,cs,se,Te,Zt,_n,Ta,eo,qa,ds,ae,kn,za,S,Ea,ht,xa,ja,mt,Fa,Ma,no,Pa,Na,to,Aa,Ia,ps,re,qe,oo,bn,Oa,so,Sa,hs,L,vn,La,ie,Da,ft,Wa,Ba,wn,Qa,Ha,Ua,le,Ra,ut,Va,Ja,gt,Ga,Ya,Xa,ze,ms,ce,Ee,ao,$n,Ka,ro,Za,fs,E,Cn,er,io,nr,tr,xe,_t,or,sr,kt,ar,rr,ir,yn,lr,bt,cr,dr,pr,K,Tn,hr,lo,mr,fr,qn,vt,ur,co,gr,_r,wt,kr,po,br,vr,je,zn,wr,En,$r,ho,Cr,yr,Tr,H,xn,qr,mo,zr,Er,Fe,xr,de,jr,fo,Fr,Mr,uo,Pr,Nr,us,pe,Me,go,jn,Ar,_o,Ir,gs,G,Fn,Or,Mn,Sr,Pn,Lr,Dr,Wr,U,Nn,Br,he,Qr,$t,Hr,Ur,ko,Rr,Vr,Jr,Pe,Gr,Ne,_s,me,Ae,bo,An,Yr,vo,Xr,ks,D,In,Kr,wo,Zr,ei,On,ni,Sn,ti,oi,si,x,Ln,ai,fe,ri,Ct,ii,li,$o,ci,di,pi,Ie,hi,Oe,mi,Se,fi,Le,ui,De,bs,ue,We,Co,Dn,gi,yo,_i,vs,W,Wn,ki,To,bi,vi,Bn,wi,Qn,$i,Ci,yi,R,Hn,Ti,ge,qi,yt,zi,Ei,qo,xi,ji,Fi,Be,Mi,Qe,ws,_e,He,zo,Un,Pi,Eo,Ni,$s,B,Rn,Ai,xo,Ii,Oi,Vn,Si,Jn,Li,Di,Wi,P,Gn,Bi,ke,Qi,Tt,Hi,Ui,jo,Ri,Vi,Ji,Ue,Gi,Re,Yi,Ve,Cs,be,Je,Fo,Yn,Xi,Mo,Ki,ys,Q,Xn,Zi,ve,el,Po,nl,tl,No,ol,sl,al,Kn,rl,Zn,il,ll,cl,N,et,dl,we,pl,qt,hl,ml,Ao,fl,ul,gl,Ge,_l,Ye,kl,Xe,Ts;return a=new Y({}),rn=new Y({}),fn=new Y({}),un=new O({props:{code:`from transformers import CanineModel
import torch

model = CanineModel.from_pretrained("google/canine-c")  # model pre-trained with autoregressive character loss

text = "hello world"
# use Python's built-in ord() function to turn each character into its unicode code point id
input_ids = torch.tensor([[ord(char) for char in text]])

outputs = model(input_ids)  # forward pass
pooled_output = outputs.pooler_output
sequence_output = 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> CanineModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>model = CanineModel.from_pretrained(<span class="hljs-string">&quot;google/canine-c&quot;</span>)  <span class="hljs-comment"># model pre-trained with autoregressive character loss</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;hello world&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># use Python&#x27;s built-in ord() function to turn each character into its unicode code point id</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = torch.tensor([[<span class="hljs-built_in">ord</span>(char) <span class="hljs-keyword">for</span> char <span class="hljs-keyword">in</span> text]])

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids)  <span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pooled_output = outputs.pooler_output
<span class="hljs-meta">&gt;&gt;&gt; </span>sequence_output = outputs.last_hidden_state`}}),gn=new O({props:{code:`from transformers import CanineTokenizer, CanineModel

model = CanineModel.from_pretrained("google/canine-c")
tokenizer = CanineTokenizer.from_pretrained("google/canine-c")

inputs = ["Life is like a box of chocolates.", "You never know what you gonna get."]
encoding = tokenizer(inputs, padding="longest", truncation=True, return_tensors="pt")

outputs = model(**encoding)  # forward pass
pooled_output = outputs.pooler_output
sequence_output = 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> CanineTokenizer, CanineModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = CanineModel.from_pretrained(<span class="hljs-string">&quot;google/canine-c&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = CanineTokenizer.from_pretrained(<span class="hljs-string">&quot;google/canine-c&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = [<span class="hljs-string">&quot;Life is like a box of chocolates.&quot;</span>, <span class="hljs-string">&quot;You never know what you gonna get.&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = tokenizer(inputs, padding=<span class="hljs-string">&quot;longest&quot;</span>, truncation=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**encoding)  <span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pooled_output = outputs.pooler_output
<span class="hljs-meta">&gt;&gt;&gt; </span>sequence_output = outputs.last_hidden_state`}}),_n=new Y({}),kn=new F({props:{name:"class transformers.models.canine.modeling_canine.CanineModelOutputWithPooling",anchor:"transformers.models.canine.modeling_canine.CanineModelOutputWithPooling",parameters:[{name:"last_hidden_state",val:": FloatTensor = None"},{name:"pooler_output",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.canine.modeling_canine.CanineModelOutputWithPooling.last_hidden_state",description:`<strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) &#x2014;
Sequence of hidden-states at the output of the last layer of the model (i.e. the output of the final
shallow Transformer encoder).`,name:"last_hidden_state"},{anchor:"transformers.models.canine.modeling_canine.CanineModelOutputWithPooling.pooler_output",description:`<strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) &#x2014;
Hidden-state of the first token of the sequence (classification token) at the last layer of the deep
Transformer encoder, 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.`,name:"pooler_output"},{anchor:"transformers.models.canine.modeling_canine.CanineModelOutputWithPooling.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 input to each encoder + one for the output of each layer of each
encoder) of shape <code>(batch_size, sequence_length, hidden_size)</code> and <code>(batch_size, sequence_length // config.downsampling_rate, hidden_size)</code>. Hidden-states of the model at the output of each layer plus the
initial input to each Transformer encoder. The hidden states of the shallow encoders have length
<code>sequence_length</code>, but the hidden states of the deep encoder have length <code>sequence_length</code> //
<code>config.downsampling_rate</code>.`,name:"hidden_states"},{anchor:"transformers.models.canine.modeling_canine.CanineModelOutputWithPooling.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 the 3 Transformer encoders of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code> and <code>(batch_size, num_heads, sequence_length // config.downsampling_rate, sequence_length // config.downsampling_rate)</code>. 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/canine/modeling_canine.py#L67"}}),bn=new Y({}),vn=new F({props:{name:"class transformers.CanineConfig",anchor:"transformers.CanineConfig",parameters:[{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.1"},{name:"attention_probs_dropout_prob",val:" = 0.1"},{name:"max_position_embeddings",val:" = 16384"},{name:"type_vocab_size",val:" = 16"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"use_cache",val:" = True"},{name:"is_encoder_decoder",val:" = False"},{name:"pad_token_id",val:" = 0"},{name:"bos_token_id",val:" = 57344"},{name:"eos_token_id",val:" = 57345"},{name:"downsampling_rate",val:" = 4"},{name:"upsampling_kernel_size",val:" = 4"},{name:"num_hash_functions",val:" = 8"},{name:"num_hash_buckets",val:" = 16384"},{name:"local_transformer_stride",val:" = 128"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CanineConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimension of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.CanineConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the deep Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.CanineConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoders.`,name:"num_attention_heads"},{anchor:"transformers.CanineConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimension of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoders.`,name:"intermediate_size"},{anchor:"transformers.CanineConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.CanineConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probabilitiy for all fully connected layers in the embeddings, encoders, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.CanineConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.CanineConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 16384) &#x2014;
The maximum sequence length that this model might ever be used with.`,name:"max_position_embeddings"},{anchor:"transformers.CanineConfig.type_vocab_size",description:`<strong>type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
The vocabulary size of the <code>token_type_ids</code> passed when calling <a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineModel">CanineModel</a>.`,name:"type_vocab_size"},{anchor:"transformers.CanineConfig.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.CanineConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.CanineConfig.downsampling_rate",description:`<strong>downsampling_rate</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
The rate at which to downsample the original character sequence length before applying the deep Transformer
encoder.`,name:"downsampling_rate"},{anchor:"transformers.CanineConfig.upsampling_kernel_size",description:`<strong>upsampling_kernel_size</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
The kernel size (i.e. the number of characters in each window) of the convolutional projection layer when
projecting back from <code>hidden_size</code>*2 to <code>hidden_size</code>.`,name:"upsampling_kernel_size"},{anchor:"transformers.CanineConfig.num_hash_functions",description:`<strong>num_hash_functions</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
The number of hash functions to use. Each hash function has its own embedding matrix.`,name:"num_hash_functions"},{anchor:"transformers.CanineConfig.num_hash_buckets",description:`<strong>num_hash_buckets</strong> (<code>int</code>, <em>optional</em>, defaults to 16384) &#x2014;
The number of hash buckets to use.`,name:"num_hash_buckets"},{anchor:"transformers.CanineConfig.local_transformer_stride",description:`<strong>local_transformer_stride</strong> (<code>int</code>, <em>optional</em>, defaults to 128) &#x2014;
The stride of the local attention of the first shallow Transformer encoder. Defaults to 128 for good
TPU/XLA memory alignment.`,name:"local_transformer_stride"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/canine/configuration_canine.py#L29"}}),ze=new V({props:{anchor:"transformers.CanineConfig.example",$$slots:{default:[ed]},$$scope:{ctx:q}}}),$n=new Y({}),Cn=new F({props:{name:"class transformers.CanineTokenizer",anchor:"transformers.CanineTokenizer",parameters:[{name:"bos_token",val:" = '\\ue000'"},{name:"eos_token",val:" = '\\ue001'"},{name:"sep_token",val:" = '\\ue001'"},{name:"cls_token",val:" = '\\ue000'"},{name:"pad_token",val:" = '\\x00'"},{name:"mask_token",val:" = '\\ue003'"},{name:"add_prefix_space",val:" = False"},{name:"model_max_length",val:" = 2048"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CanineTokenizer.model_max_length",description:`<strong>model_max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
The maximum sentence length the model accepts.`,name:"model_max_length"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/canine/tokenization_canine.py#L63"}}),Tn=new F({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.CanineTokenizer.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.CanineTokenizer.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.CanineTokenizer.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/canine/tokenization_canine.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 F({props:{name:"get_special_tokens_mask",anchor:"transformers.CanineTokenizer.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.CanineTokenizer.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.CanineTokenizer.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.CanineTokenizer.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/canine/tokenization_canine.py#L182",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>
`}}),xn=new F({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.CanineTokenizer.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.CanineTokenizer.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.CanineTokenizer.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/canine/tokenization_canine.py#L210",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>
`}}),Fe=new V({props:{anchor:"transformers.CanineTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[nd]},$$scope:{ctx:q}}}),jn=new Y({}),Fn=new F({props:{name:"class transformers.CanineModel",anchor:"transformers.CanineModel",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = True"}],parametersDescription:[{anchor:"transformers.CanineModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</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/canine/modeling_canine.py#L993"}}),Nn=new F({props:{name:"forward",anchor:"transformers.CanineModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_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.CanineModel.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/canine#transformers.CanineTokenizer">CanineTokenizer</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.CanineModel.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.CanineModel.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.CanineModel.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.CanineModel.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.CanineModel.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.CanineModel.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.CanineModel.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.CanineModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.25.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/canine/modeling_canine.py#L1101",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.models.canine.modeling_canine.CanineModelOutputWithPooling"
>transformers.models.canine.modeling_canine.CanineModelOutputWithPooling</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/canine#transformers.CanineConfig"
>CanineConfig</a>) and inputs.</p>
<ul>
<li><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 (i.e. the output of the final
shallow Transformer encoder).</li>
<li><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Hidden-state of the first token of the sequence (classification token) at the last layer of the deep
Transformer encoder, 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.</li>
<li><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 input to each encoder + one for the output of each layer of each
encoder) of shape <code>(batch_size, sequence_length, hidden_size)</code> and <code>(batch_size, sequence_length // config.downsampling_rate, hidden_size)</code>. Hidden-states of the model at the output of each layer plus the
initial input to each Transformer encoder. The hidden states of the shallow encoders have length
<code>sequence_length</code>, but the hidden states of the deep encoder have length <code>sequence_length</code> //
<code>config.downsampling_rate</code>.</li>
<li><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 the 3 Transformer encoders of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code> and <code>(batch_size, num_heads, sequence_length // config.downsampling_rate, sequence_length // config.downsampling_rate)</code>. Attentions weights after the
attention softmax, used to compute the weighted average in the self-attention heads.</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.models.canine.modeling_canine.CanineModelOutputWithPooling"
>transformers.models.canine.modeling_canine.CanineModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Pe=new Yo({props:{$$slots:{default:[td]},$$scope:{ctx:q}}}),Ne=new V({props:{anchor:"transformers.CanineModel.forward.example",$$slots:{default:[od]},$$scope:{ctx:q}}}),An=new Y({}),In=new F({props:{name:"class transformers.CanineForSequenceClassification",anchor:"transformers.CanineForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.CanineForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</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/canine/modeling_canine.py#L1271"}}),Ln=new F({props:{name:"forward",anchor:"transformers.CanineForSequenceClassification.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.CanineForSequenceClassification.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/canine#transformers.CanineTokenizer">CanineTokenizer</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.CanineForSequenceClassification.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.CanineForSequenceClassification.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.CanineForSequenceClassification.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.CanineForSequenceClassification.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.CanineForSequenceClassification.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.CanineForSequenceClassification.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.CanineForSequenceClassification.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.CanineForSequenceClassification.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.CanineForSequenceClassification.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/canine/modeling_canine.py#L1283",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/canine#transformers.CanineConfig"
>CanineConfig</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>
`}}),Ie=new Yo({props:{$$slots:{default:[sd]},$$scope:{ctx:q}}}),Oe=new V({props:{anchor:"transformers.CanineForSequenceClassification.forward.example",$$slots:{default:[ad]},$$scope:{ctx:q}}}),Se=new V({props:{anchor:"transformers.CanineForSequenceClassification.forward.example-2",$$slots:{default:[rd]},$$scope:{ctx:q}}}),Le=new V({props:{anchor:"transformers.CanineForSequenceClassification.forward.example-3",$$slots:{default:[id]},$$scope:{ctx:q}}}),De=new V({props:{anchor:"transformers.CanineForSequenceClassification.forward.example-4",$$slots:{default:[ld]},$$scope:{ctx:q}}}),Dn=new Y({}),Wn=new F({props:{name:"class transformers.CanineForMultipleChoice",anchor:"transformers.CanineForMultipleChoice",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.CanineForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</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/canine/modeling_canine.py#L1371"}}),Hn=new F({props:{name:"forward",anchor:"transformers.CanineForMultipleChoice.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.CanineForMultipleChoice.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/canine#transformers.CanineTokenizer">CanineTokenizer</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.CanineForMultipleChoice.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.CanineForMultipleChoice.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.CanineForMultipleChoice.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.CanineForMultipleChoice.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.CanineForMultipleChoice.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.CanineForMultipleChoice.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.CanineForMultipleChoice.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.CanineForMultipleChoice.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.CanineForMultipleChoice.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/canine/modeling_canine.py#L1382",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/canine#transformers.CanineConfig"
>CanineConfig</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>
`}}),Be=new Yo({props:{$$slots:{default:[cd]},$$scope:{ctx:q}}}),Qe=new V({props:{anchor:"transformers.CanineForMultipleChoice.forward.example",$$slots:{default:[dd]},$$scope:{ctx:q}}}),Un=new Y({}),Rn=new F({props:{name:"class transformers.CanineForTokenClassification",anchor:"transformers.CanineForTokenClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.CanineForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</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/canine/modeling_canine.py#L1463"}}),Gn=new F({props:{name:"forward",anchor:"transformers.CanineForTokenClassification.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.CanineForTokenClassification.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/canine#transformers.CanineTokenizer">CanineTokenizer</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.CanineForTokenClassification.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.CanineForTokenClassification.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.CanineForTokenClassification.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.CanineForTokenClassification.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.CanineForTokenClassification.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.CanineForTokenClassification.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.CanineForTokenClassification.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.CanineForTokenClassification.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.CanineForTokenClassification.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/canine/modeling_canine.py#L1475",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/canine#transformers.CanineConfig"
>CanineConfig</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>
`}}),Ue=new Yo({props:{$$slots:{default:[pd]},$$scope:{ctx:q}}}),Re=new V({props:{anchor:"transformers.CanineForTokenClassification.forward.example",$$slots:{default:[hd]},$$scope:{ctx:q}}}),Ve=new V({props:{anchor:"transformers.CanineForTokenClassification.forward.example-2",$$slots:{default:[md]},$$scope:{ctx:q}}}),Yn=new Y({}),Xn=new F({props:{name:"class transformers.CanineForQuestionAnswering",anchor:"transformers.CanineForQuestionAnswering",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.CanineForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</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/canine/modeling_canine.py#L1569"}}),et=new F({props:{name:"forward",anchor:"transformers.CanineForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"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.CanineForQuestionAnswering.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/canine#transformers.CanineTokenizer">CanineTokenizer</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.CanineForQuestionAnswering.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.CanineForQuestionAnswering.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.CanineForQuestionAnswering.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.CanineForQuestionAnswering.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.CanineForQuestionAnswering.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.CanineForQuestionAnswering.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.CanineForQuestionAnswering.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.CanineForQuestionAnswering.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.CanineForQuestionAnswering.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.CanineForQuestionAnswering.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/canine/modeling_canine.py#L1580",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput"
>transformers.modeling_outputs.QuestionAnsweringModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineConfig"
>CanineConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.</p>
</li>
<li>
<p><strong>start_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-start scores (before SoftMax).</p>
</li>
<li>
<p><strong>end_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-end scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.25.1/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput"
>transformers.modeling_outputs.QuestionAnsweringModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ge=new Yo({props:{$$slots:{default:[fd]},$$scope:{ctx:q}}}),Ye=new V({props:{anchor:"transformers.CanineForQuestionAnswering.forward.example",$$slots:{default:[ud]},$$scope:{ctx:q}}}),Xe=new V({props:{anchor:"transformers.CanineForQuestionAnswering.forward.example-2",$$slots:{default:[gd]},$$scope:{ctx:q}}}),{c(){c=o("meta"),k=p(),d=o("h1"),m=o("a"),b=o("span"),v(a.$$.fragment),g=p(),z=o("span"),Rs=i("CANINE"),Xo=p(),te=o("h2"),$e=o("a"),Lt=o("span"),v(rn.$$.fragment),Vs=p(),Dt=o("span"),Js=i("Overview"),Ko=p(),Ce=o("p"),Gs=i("The CANINE model was proposed in "),ln=o("a"),Ys=i(`CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language
Representation`),Xs=i(` by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting. It\u2019s
among the first papers that trains a Transformer without using an explicit tokenization step (such as Byte Pair
Encoding (BPE), WordPiece or SentencePiece). Instead, the model is trained directly at a Unicode character-level.
Training at a character-level inevitably comes with a longer sequence length, which CANINE solves with an efficient
downsampling strategy, before applying a deep Transformer encoder.`),Zo=p(),st=o("p"),Ks=i("The abstract from the paper is the following:"),es=p(),at=o("p"),Wt=o("em"),Zs=i(`Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models
still require an explicit tokenization step. While recent tokenization approaches based on data-derived subword
lexicons are less brittle than manually engineered tokenizers, these techniques are not equally suited to all
languages, and the use of any fixed vocabulary may limit a model\u2019s ability to adapt. In this paper, we present CANINE,
a neural encoder that operates directly on character sequences, without explicit tokenization or vocabulary, and a
pre-training strategy that operates either directly on characters or optionally uses subwords as a soft inductive bias.
To use its finer-grained input effectively and efficiently, CANINE combines downsampling, which reduces the input
sequence length, with a deep transformer stack, which encodes context. CANINE outperforms a comparable mBERT model by
2.8 F1 on TyDi QA, a challenging multilingual benchmark, despite having 28% fewer model parameters.`),ns=p(),rt=o("p"),ea=i("Tips:"),ts=p(),j=o("ul"),Bt=o("li"),Qt=o("p"),na=i(`CANINE uses no less than 3 Transformer encoders internally: 2 \u201Cshallow\u201D encoders (which only consist of a single
layer) and 1 \u201Cdeep\u201D encoder (which is a regular BERT encoder). First, a \u201Cshallow\u201D encoder is used to contextualize
the character embeddings, using local attention. Next, after downsampling, a \u201Cdeep\u201D encoder is applied. Finally,
after upsampling, a \u201Cshallow\u201D encoder is used to create the final character embeddings. Details regarding up- and
downsampling can be found in the paper.`),ta=p(),Ht=o("li"),cn=o("p"),oa=i("CANINE uses a max sequence length of 2048 characters by default. One can use "),it=o("a"),sa=i("CanineTokenizer"),aa=i(`
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tokens needs to be upsampled again to match the length of the original character sequence (which is 2048). The
details for this can be found in the paper.`),la=p(),Vt=o("li"),Jt=o("p"),ca=i("Models:"),da=p(),Gt=o("li"),lt=o("p"),dn=o("a"),pa=i("google/canine-c"),ha=i(`: Pre-trained with autoregressive character loss,
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output) e.g. for GLUE tasks.`),ei=p(),On=o("p"),ni=i("This model is a PyTorch "),Sn=o("a"),ti=i("torch.nn.Module"),oi=i(` sub-class. Use
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softmax) e.g. for RocStories/SWAG tasks.`),vi=p(),Bn=o("p"),wi=i("This model is a PyTorch "),Qn=o("a"),$i=i("torch.nn.Module"),Ci=i(` sub-class. Use
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lexicons are less brittle than manually engineered tokenizers, these techniques are not equally suited to all
languages, and the use of any fixed vocabulary may limit a model\u2019s ability to adapt. In this paper, we present CANINE,
a neural encoder that operates directly on character sequences, without explicit tokenization or vocabulary, and a
pre-training strategy that operates either directly on characters or optionally uses subwords as a soft inductive bias.
To use its finer-grained input effectively and efficiently, CANINE combines downsampling, which reduces the input
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This model is a PyTorch `),Pn=s(Os,"A",{href:!0,rel:!0});var _c=r(Pn);Lr=l(_c,"torch.nn.Module"),_c.forEach(t),Dr=l(Os,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Os.forEach(t),Wr=h(Mt),U=s(Mt,"DIV",{class:!0});var en=r(U);w(Nn.$$.fragment,en),Br=h(en),he=s(en,"P",{});var Pt=r(he);Qr=l(Pt,"The "),$t=s(Pt,"A",{href:!0});var kc=r($t);Hr=l(kc,"CanineModel"),kc.forEach(t),Ur=l(Pt," forward method, overrides the "),ko=s(Pt,"CODE",{});var bc=r(ko);Rr=l(bc,"__call__"),bc.forEach(t),Vr=l(Pt," special method."),Pt.forEach(t),Jr=h(en),w(Pe.$$.fragment,en),Gr=h(en),w(Ne.$$.fragment,en),en.forEach(t),Mt.forEach(t),_s=h(n),me=s(n,"H2",{class:!0});var Ss=r(me);Ae=s(Ss,"A",{id:!0,class:!0,href:!0});var vc=r(Ae);bo=s(vc,"SPAN",{});var wc=r(bo);w(An.$$.fragment,wc),wc.forEach(t),vc.forEach(t),Yr=h(Ss),vo=s(Ss,"SPAN",{});var $c=r(vo);Xr=l($c,"CanineForSequenceClassification"),$c.forEach(t),Ss.forEach(t),ks=h(n),D=s(n,"DIV",{class:!0});var nn=r(D);w(In.$$.fragment,nn),Kr=h(nn),wo=s(nn,"P",{});var Cc=r(wo);Zr=l(Cc,`CANINE Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.`),Cc.forEach(t),ei=h(nn),On=s(nn,"P",{});var Ls=r(On);ni=l(Ls,"This model is a PyTorch "),Sn=s(Ls,"A",{href:!0,rel:!0});var yc=r(Sn);ti=l(yc,"torch.nn.Module"),yc.forEach(t),oi=l(Ls,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Ls.forEach(t),si=h(nn),x=s(nn,"DIV",{class:!0});var I=r(x);w(Ln.$$.fragment,I),ai=h(I),fe=s(I,"P",{});var Nt=r(fe);ri=l(Nt,"The "),Ct=s(Nt,"A",{href:!0});var Tc=r(Ct);ii=l(Tc,"CanineForSequenceClassification"),Tc.forEach(t),li=l(Nt," forward method, overrides the "),$o=s(Nt,"CODE",{});var qc=r($o);ci=l(qc,"__call__"),qc.forEach(t),di=l(Nt," special method."),Nt.forEach(t),pi=h(I),w(Ie.$$.fragment,I),hi=h(I),w(Oe.$$.fragment,I),mi=h(I),w(Se.$$.fragment,I),fi=h(I),w(Le.$$.fragment,I),ui=h(I),w(De.$$.fragment,I),I.forEach(t),nn.forEach(t),bs=h(n),ue=s(n,"H2",{class:!0});var Ds=r(ue);We=s(Ds,"A",{id:!0,class:!0,href:!0});var zc=r(We);Co=s(zc,"SPAN",{});var Ec=r(Co);w(Dn.$$.fragment,Ec),Ec.forEach(t),zc.forEach(t),gi=h(Ds),yo=s(Ds,"SPAN",{});var xc=r(yo);_i=l(xc,"CanineForMultipleChoice"),xc.forEach(t),Ds.forEach(t),vs=h(n),W=s(n,"DIV",{class:!0});var tn=r(W);w(Wn.$$.fragment,tn),ki=h(tn),To=s(tn,"P",{});var jc=r(To);bi=l(jc,`CANINE Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.`),jc.forEach(t),vi=h(tn),Bn=s(tn,"P",{});var Ws=r(Bn);wi=l(Ws,"This model is a PyTorch "),Qn=s(Ws,"A",{href:!0,rel:!0});var Fc=r(Qn);$i=l(Fc,"torch.nn.Module"),Fc.forEach(t),Ci=l(Ws,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Ws.forEach(t),yi=h(tn),R=s(tn,"DIV",{class:!0});var on=r(R);w(Hn.$$.fragment,on),Ti=h(on),ge=s(on,"P",{});var At=r(ge);qi=l(At,"The "),yt=s(At,"A",{href:!0});var Mc=r(yt);zi=l(Mc,"CanineForMultipleChoice"),Mc.forEach(t),Ei=l(At," forward method, overrides the "),qo=s(At,"CODE",{});var Pc=r(qo);xi=l(Pc,"__call__"),Pc.forEach(t),ji=l(At," special method."),At.forEach(t),Fi=h(on),w(Be.$$.fragment,on),Mi=h(on),w(Qe.$$.fragment,on),on.forEach(t),tn.forEach(t),ws=h(n),_e=s(n,"H2",{class:!0});var Bs=r(_e);He=s(Bs,"A",{id:!0,class:!0,href:!0});var Nc=r(He);zo=s(Nc,"SPAN",{});var Ac=r(zo);w(Un.$$.fragment,Ac),Ac.forEach(t),Nc.forEach(t),Pi=h(Bs),Eo=s(Bs,"SPAN",{});var Ic=r(Eo);Ni=l(Ic,"CanineForTokenClassification"),Ic.forEach(t),Bs.forEach(t),$s=h(n),B=s(n,"DIV",{class:!0});var sn=r(B);w(Rn.$$.fragment,sn),Ai=h(sn),xo=s(sn,"P",{});var Oc=r(xo);Ii=l(Oc,`CANINE Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
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it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Qs.forEach(t),Wi=h(sn),P=s(sn,"DIV",{class:!0});var ee=r(P);w(Gn.$$.fragment,ee),Bi=h(ee),ke=s(ee,"P",{});var It=r(ke);Qi=l(It,"The "),Tt=s(It,"A",{href:!0});var Lc=r(Tt);Hi=l(Lc,"CanineForTokenClassification"),Lc.forEach(t),Ui=l(It," forward method, overrides the "),jo=s(It,"CODE",{});var Dc=r(jo);Ri=l(Dc,"__call__"),Dc.forEach(t),Vi=l(It," special method."),It.forEach(t),Ji=h(ee),w(Ue.$$.fragment,ee),Gi=h(ee),w(Re.$$.fragment,ee),Yi=h(ee),w(Ve.$$.fragment,ee),ee.forEach(t),sn.forEach(t),Cs=h(n),be=s(n,"H2",{class:!0});var Hs=r(be);Je=s(Hs,"A",{id:!0,class:!0,href:!0});var Wc=r(Je);Fo=s(Wc,"SPAN",{});var Bc=r(Fo);w(Yn.$$.fragment,Bc),Bc.forEach(t),Wc.forEach(t),Xi=h(Hs),Mo=s(Hs,"SPAN",{});var Qc=r(Mo);Ki=l(Qc,"CanineForQuestionAnswering"),Qc.forEach(t),Hs.forEach(t),ys=h(n),Q=s(n,"DIV",{class:!0});var an=r(Q);w(Xn.$$.fragment,an),Zi=h(an),ve=s(an,"P",{});var Ot=r(ve);el=l(Ot,`CANINE Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
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behavior.`),Us.forEach(t),cl=h(an),N=s(an,"DIV",{class:!0});var ne=r(N);w(et.$$.fragment,ne),dl=h(ne),we=s(ne,"P",{});var St=r(we);pl=l(St,"The "),qt=s(St,"A",{href:!0});var Vc=r(qt);hl=l(Vc,"CanineForQuestionAnswering"),Vc.forEach(t),ml=l(St," forward method, overrides the "),Ao=s(St,"CODE",{});var Jc=r(Ao);fl=l(Jc,"__call__"),Jc.forEach(t),ul=l(St," special method."),St.forEach(t),gl=h(ne),w(Ge.$$.fragment,ne),_l=h(ne),w(Ye.$$.fragment,ne),kl=h(ne),w(Xe.$$.fragment,ne),ne.forEach(t),an.forEach(t),this.h()},h(){f(c,"name","hf:doc:metadata"),f(c,"content",JSON.stringify(kd)),f(m,"id","canine"),f(m,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),f(m,"href","#canine"),f(d,"class","relative group"),f($e,"id","overview"),f($e,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 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