import{S as Ol,i as Rl,s as Gl,e as u,k as m,w as g,t as h,M as Ul,c,d as l,m as _,x as v,a as f,h as d,b as y,G as a,g as p,y as w,o as $,p as me,q as k,B as q,v as Yl,n as he}from"../../chunks/vendor-hf-doc-builder.js";import{T as Ll}from"../../chunks/Tip-hf-doc-builder.js";import{Y as Hl}from"../../chunks/Youtube-hf-doc-builder.js";import{I as Hs}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as E}from"../../chunks/CodeBlock-hf-doc-builder.js";import{C as Bl}from"../../chunks/CourseFloatingBanner-hf-doc-builder.js";import{F as Jl}from"../../chunks/FrameworkSwitchCourse-hf-doc-builder.js";import"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function Ql(j){let n,o;return n=new Bl({props:{chapter:2,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter2/section5_tf.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter2/section5_tf.ipynb"}]}}),{c(){g(n.$$.fragment)},l(s){v(n.$$.fragment,s)},m(s,i){w(n,s,i),o=!0},i(s){o||(k(n.$$.fragment,s),o=!0)},o(s){$(n.$$.fragment,s),o=!1},d(s){q(n,s)}}}function Kl(j){let n,o;return n=new Bl({props:{chapter:2,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter2/section5_pt.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter2/section5_pt.ipynb"}]}}),{c(){g(n.$$.fragment)},l(s){v(n.$$.fragment,s)},m(s,i){w(n,s,i),o=!0},i(s){o||(k(n.$$.fragment,s),o=!0)},o(s){$(n.$$.fragment,s),o=!1},d(s){q(n,s)}}}function Vl(j){let n,o;return n=new Hl({props:{id:"ROxrFOEbsQE"}}),{c(){g(n.$$.fragment)},l(s){v(n.$$.fragment,s)},m(s,i){w(n,s,i),o=!0},i(s){o||(k(n.$$.fragment,s),o=!0)},o(s){$(n.$$.fragment,s),o=!1},d(s){q(n,s)}}}function Wl(j){let n,o;return n=new Hl({props:{id:"M6adb1j2jPI"}}),{c(){g(n.$$.fragment)},l(s){v(n.$$.fragment,s)},m(s,i){w(n,s,i),o=!0},i(s){o||(k(n.$$.fragment,s),o=!0)},o(s){$(n.$$.fragment,s),o=!1},d(s){q(n,s)}}}function Xl(j){let n,o,s,i;return n=new E({props:{code:`import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)

sequence = "I've been waiting for a HuggingFace course my whole life."

tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = tf.constant(ids)
# \uC774 \uCF54\uB4DC\uB294 \uC2E4\uD589\uB418\uC9C0 \uC54A\uC744 \uAC83\uC785\uB2C8\uB2E4.
model(input_ids)`,highlighted:`<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForSequenceClassification

checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)

sequence = <span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>

tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = tf.constant(ids)
<span class="hljs-comment"># \uC774 \uCF54\uB4DC\uB294 \uC2E4\uD589\uB418\uC9C0 \uC54A\uC744 \uAC83\uC785\uB2C8\uB2E4.</span>
model(input_ids)`}}),s=new E({props:{code:"InvalidArgumentError: Input to reshape is a tensor with 14 values, but the requested shape has 196 [Op:Reshape]",highlighted:'InvalidArgumentError: Input to reshape <span class="hljs-keyword">is</span> a tensor <span class="hljs-keyword">with</span> <span class="hljs-number">14</span> values, but the requested shape has <span class="hljs-number">196</span> [Op:Reshape]'}}),{c(){g(n.$$.fragment),o=m(),g(s.$$.fragment)},l(t){v(n.$$.fragment,t),o=_(t),v(s.$$.fragment,t)},m(t,b){w(n,t,b),p(t,o,b),w(s,t,b),i=!0},i(t){i||(k(n.$$.fragment,t),k(s.$$.fragment,t),i=!0)},o(t){$(n.$$.fragment,t),$(s.$$.fragment,t),i=!1},d(t){q(n,t),t&&l(o),q(s,t)}}}function Zl(j){let n,o,s,i;return n=new E({props:{code:`import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)

sequence = "I've been waiting for a HuggingFace course my whole life."

tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = torch.tensor(ids)
# \uC774 \uCF54\uB4DC\uB294 \uC2E4\uD589\uB418\uC9C0 \uC54A\uC744 \uAC83\uC785\uB2C8\uB2E4.
model(input_ids)`,highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSequenceClassification

checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)

sequence = <span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>

tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = torch.tensor(ids)
<span class="hljs-comment"># \uC774 \uCF54\uB4DC\uB294 \uC2E4\uD589\uB418\uC9C0 \uC54A\uC744 \uAC83\uC785\uB2C8\uB2E4.</span>
model(input_ids)`}}),s=new E({props:{code:"IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)",highlighted:'IndexError: Dimension out of <span class="hljs-built_in">range</span> (expected to be <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span> of [-<span class="hljs-number">1</span>, <span class="hljs-number">0</span>], but got <span class="hljs-number">1</span>)'}}),{c(){g(n.$$.fragment),o=m(),g(s.$$.fragment)},l(t){v(n.$$.fragment,t),o=_(t),v(s.$$.fragment,t)},m(t,b){w(n,t,b),p(t,o,b),w(s,t,b),i=!0},i(t){i||(k(n.$$.fragment,t),k(s.$$.fragment,t),i=!0)},o(t){$(n.$$.fragment,t),$(s.$$.fragment,t),i=!1},d(t){q(n,t),t&&l(o),q(s,t)}}}function ea(j){let n,o,s,i;return n=new E({props:{code:`tokenized_inputs = tokenizer(sequence, return_tensors="tf")
print(tokenized_inputs["input_ids"])`,highlighted:`tokenized_inputs = tokenizer(sequence, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-built_in">print</span>(tokenized_inputs[<span class="hljs-string">&quot;input_ids&quot;</span>])`}}),s=new E({props:{code:`<tf.Tensor: shape=(1, 16), dtype=int32, numpy=
array([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662,
        12172,  2607,  2026,  2878,  2166,  1012,   102]], dtype=int32)>`,highlighted:`&lt;tf.Tensor: shape=(<span class="hljs-number">1</span>, <span class="hljs-number">16</span>), dtype=int32, numpy=
array([[  <span class="hljs-number">101</span>,  <span class="hljs-number">1045</span>,  <span class="hljs-number">1005</span>,  <span class="hljs-number">2310</span>,  <span class="hljs-number">2042</span>,  <span class="hljs-number">3403</span>,  <span class="hljs-number">2005</span>,  <span class="hljs-number">1037</span>, <span class="hljs-number">17662</span>,
        <span class="hljs-number">12172</span>,  <span class="hljs-number">2607</span>,  <span class="hljs-number">2026</span>,  <span class="hljs-number">2878</span>,  <span class="hljs-number">2166</span>,  <span class="hljs-number">1012</span>,   <span class="hljs-number">102</span>]], dtype=int32)&gt;`}}),{c(){g(n.$$.fragment),o=m(),g(s.$$.fragment)},l(t){v(n.$$.fragment,t),o=_(t),v(s.$$.fragment,t)},m(t,b){w(n,t,b),p(t,o,b),w(s,t,b),i=!0},i(t){i||(k(n.$$.fragment,t),k(s.$$.fragment,t),i=!0)},o(t){$(n.$$.fragment,t),$(s.$$.fragment,t),i=!1},d(t){q(n,t),t&&l(o),q(s,t)}}}function sa(j){let n,o,s,i;return n=new E({props:{code:`tokenized_inputs = tokenizer(sequence, return_tensors="pt")
print(tokenized_inputs["input_ids"])`,highlighted:`tokenized_inputs = tokenizer(sequence, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-built_in">print</span>(tokenized_inputs[<span class="hljs-string">&quot;input_ids&quot;</span>])`}}),s=new E({props:{code:`tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,
          2607,  2026,  2878,  2166,  1012,   102]])`,highlighted:`tensor([[  <span class="hljs-number">101</span>,  <span class="hljs-number">1045</span>,  <span class="hljs-number">1005</span>,  <span class="hljs-number">2310</span>,  <span class="hljs-number">2042</span>,  <span class="hljs-number">3403</span>,  <span class="hljs-number">2005</span>,  <span class="hljs-number">1037</span>, <span class="hljs-number">17662</span>, <span class="hljs-number">12172</span>,
          <span class="hljs-number">2607</span>,  <span class="hljs-number">2026</span>,  <span class="hljs-number">2878</span>,  <span class="hljs-number">2166</span>,  <span class="hljs-number">1012</span>,   <span class="hljs-number">102</span>]])`}}),{c(){g(n.$$.fragment),o=m(),g(s.$$.fragment)},l(t){v(n.$$.fragment,t),o=_(t),v(s.$$.fragment,t)},m(t,b){w(n,t,b),p(t,o,b),w(s,t,b),i=!0},i(t){i||(k(n.$$.fragment,t),k(s.$$.fragment,t),i=!0)},o(t){$(n.$$.fragment,t),$(s.$$.fragment,t),i=!1},d(t){q(n,t),t&&l(o),q(s,t)}}}function ta(j){let n,o;return n=new E({props:{code:`import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)

sequence = "I've been waiting for a HuggingFace course my whole life."

tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)

input_ids = tf.constant([ids])
print("Input IDs:", input_ids)

output = model(input_ids)
print("Logits:", output.logits)`,highlighted:`<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForSequenceClassification

checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)

sequence = <span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>

tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)

input_ids = tf.constant([ids])
<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Input IDs:&quot;</span>, input_ids)

output = model(input_ids)
<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Logits:&quot;</span>, output.logits)`}}),{c(){g(n.$$.fragment)},l(s){v(n.$$.fragment,s)},m(s,i){w(n,s,i),o=!0},i(s){o||(k(n.$$.fragment,s),o=!0)},o(s){$(n.$$.fragment,s),o=!1},d(s){q(n,s)}}}function na(j){let n,o;return n=new E({props:{code:`import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)

sequence = "I've been waiting for a HuggingFace course my whole life."

tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)

input_ids = torch.tensor([ids])
print("Input IDs:", input_ids)

output = model(input_ids)
print("Logits:", output.logits)`,highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSequenceClassification

checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)

sequence = <span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>

tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)

input_ids = torch.tensor([ids])
<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Input IDs:&quot;</span>, input_ids)

output = model(input_ids)
<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Logits:&quot;</span>, output.logits)`}}),{c(){g(n.$$.fragment)},l(s){v(n.$$.fragment,s)},m(s,i){w(n,s,i),o=!0},i(s){o||(k(n.$$.fragment,s),o=!0)},o(s){$(n.$$.fragment,s),o=!1},d(s){q(n,s)}}}function la(j){let n,o;return n=new E({props:{code:`Input IDs: tf.Tensor(
[[ 1045  1005  2310  2042  3403  2005  1037 17662 12172  2607  2026  2878
   2166  1012]], shape=(1, 14), dtype=int32)
Logits: tf.Tensor([[-2.7276208  2.8789377]], shape=(1, 2), dtype=float32)`,highlighted:`Input IDs: tf.Tensor(
[[ <span class="hljs-number">1045</span>  <span class="hljs-number">1005</span>  <span class="hljs-number">2310</span>  <span class="hljs-number">2042</span>  <span class="hljs-number">3403</span>  <span class="hljs-number">2005</span>  <span class="hljs-number">1037</span> <span class="hljs-number">17662</span> <span class="hljs-number">12172</span>  <span class="hljs-number">2607</span>  <span class="hljs-number">2026</span>  <span class="hljs-number">2878</span>
   <span class="hljs-number">2166</span>  <span class="hljs-number">1012</span>]], shape=(<span class="hljs-number">1</span>, <span class="hljs-number">14</span>), dtype=int32)
Logits: tf.Tensor([[-<span class="hljs-number">2.7276208</span>  <span class="hljs-number">2.8789377</span>]], shape=(<span class="hljs-number">1</span>, <span class="hljs-number">2</span>), dtype=float32)`}}),{c(){g(n.$$.fragment)},l(s){v(n.$$.fragment,s)},m(s,i){w(n,s,i),o=!0},i(s){o||(k(n.$$.fragment,s),o=!0)},o(s){$(n.$$.fragment,s),o=!1},d(s){q(n,s)}}}function aa(j){let n,o;return n=new E({props:{code:`Input IDs: [[ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607, 2026,  2878,  2166,  1012]]
Logits: [[-2.7276,  2.8789]]`,highlighted:`Input IDs: [[ <span class="hljs-number">1045</span>,  <span class="hljs-number">1005</span>,  <span class="hljs-number">2310</span>,  <span class="hljs-number">2042</span>,  <span class="hljs-number">3403</span>,  <span class="hljs-number">2005</span>,  <span class="hljs-number">1037</span>, <span class="hljs-number">17662</span>, <span class="hljs-number">12172</span>,  <span class="hljs-number">2607</span>, <span class="hljs-number">2026</span>,  <span class="hljs-number">2878</span>,  <span class="hljs-number">2166</span>,  <span class="hljs-number">1012</span>]]
Logits: [[-<span class="hljs-number">2.7276</span>,  <span class="hljs-number">2.8789</span>]]`}}),{c(){g(n.$$.fragment)},l(s){v(n.$$.fragment,s)},m(s,i){w(n,s,i),o=!0},i(s){o||(k(n.$$.fragment,s),o=!0)},o(s){$(n.$$.fragment,s),o=!1},d(s){q(n,s)}}}function ra(j){let n,o,s,i,t,b,z,A;return{c(){n=u("p"),o=h("\u270F\uFE0F "),s=u("strong"),i=h("\uC9C1\uC811 \uD574\uBCF4\uC138\uC694!"),t=h(" \uC774 "),b=u("code"),z=h("batched_ids"),A=h(" \uB9AC\uC2A4\uD2B8\uB97C \uD150\uC11C\uB85C \uBCC0\uD658\uD558\uACE0 \uBAA8\uB378\uB85C \uC804\uB2EC\uD574\uBCF4\uC138\uC694. \uC774\uC804\uC5D0 \uC5BB\uC740 \uB85C\uC9D3\uACFC \uB3D9\uC77C\uD55C \uACB0\uACFC\uB97C \uC5BB\uB294\uC9C0 \uD655\uC778\uD574\uBCF4\uC138\uC694. (\uAC1C\uC218\uB294 \uB450 \uAC1C\uC5EC\uC57C \uD569\uB2C8\uB2E4!)")},l(K){n=c(K,"P",{});var I=f(n);o=d(I,"\u270F\uFE0F "),s=c(I,"STRONG",{});var Ne=f(s);i=d(Ne,"\uC9C1\uC811 \uD574\uBCF4\uC138\uC694!"),Ne.forEach(l),t=d(I," \uC774 "),b=c(I,"CODE",{});var _e=f(b);z=d(_e,"batched_ids"),_e.forEach(l),A=d(I," \uB9AC\uC2A4\uD2B8\uB97C \uD150\uC11C\uB85C \uBCC0\uD658\uD558\uACE0 \uBAA8\uB378\uB85C \uC804\uB2EC\uD574\uBCF4\uC138\uC694. \uC774\uC804\uC5D0 \uC5BB\uC740 \uB85C\uC9D3\uACFC \uB3D9\uC77C\uD55C \uACB0\uACFC\uB97C \uC5BB\uB294\uC9C0 \uD655\uC778\uD574\uBCF4\uC138\uC694. (\uAC1C\uC218\uB294 \uB450 \uAC1C\uC5EC\uC57C \uD569\uB2C8\uB2E4!)"),I.forEach(l)},m(K,I){p(K,n,I),a(n,o),a(n,s),a(s,i),a(n,t),a(n,b),a(b,z),a(n,A)},d(K){K&&l(n)}}}function oa(j){let n,o,s,i;return n=new E({props:{code:`model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)

sequence1_ids = [[200, 200, 200]]
sequence2_ids = [[200, 200]]
batched_ids = [
    [200, 200, 200],
    [200, 200, tokenizer.pad_token_id],
]

print(model(tf.constant(sequence1_ids)).logits)
print(model(tf.constant(sequence2_ids)).logits)
print(model(tf.constant(batched_ids)).logits)`,highlighted:`model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)

sequence1_ids = [[<span class="hljs-number">200</span>, <span class="hljs-number">200</span>, <span class="hljs-number">200</span>]]
sequence2_ids = [[<span class="hljs-number">200</span>, <span class="hljs-number">200</span>]]
batched_ids = [
    [<span class="hljs-number">200</span>, <span class="hljs-number">200</span>, <span class="hljs-number">200</span>],
    [<span class="hljs-number">200</span>, <span class="hljs-number">200</span>, tokenizer.pad_token_id],
]

<span class="hljs-built_in">print</span>(model(tf.constant(sequence1_ids)).logits)
<span class="hljs-built_in">print</span>(model(tf.constant(sequence2_ids)).logits)
<span class="hljs-built_in">print</span>(model(tf.constant(batched_ids)).logits)`}}),s=new E({props:{code:`tf.Tensor([[ 1.5693678 -1.3894581]], shape=(1, 2), dtype=float32)
tf.Tensor([[ 0.5803005  -0.41252428]], shape=(1, 2), dtype=float32)
tf.Tensor(
[[ 1.5693681 -1.3894582]
 [ 1.3373486 -1.2163193]], shape=(2, 2), dtype=float32)`,highlighted:`tf.Tensor([[ <span class="hljs-number">1.5693678</span> -<span class="hljs-number">1.3894581</span>]], shape=(<span class="hljs-number">1</span>, <span class="hljs-number">2</span>), dtype=float32)
tf.Tensor([[ <span class="hljs-number">0.5803005</span>  -<span class="hljs-number">0.41252428</span>]], shape=(<span class="hljs-number">1</span>, <span class="hljs-number">2</span>), dtype=float32)
tf.Tensor(
[[ <span class="hljs-number">1.5693681</span> -<span class="hljs-number">1.3894582</span>]
 [ <span class="hljs-number">1.3373486</span> -<span class="hljs-number">1.2163193</span>]], shape=(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>), dtype=float32)`}}),{c(){g(n.$$.fragment),o=m(),g(s.$$.fragment)},l(t){v(n.$$.fragment,t),o=_(t),v(s.$$.fragment,t)},m(t,b){w(n,t,b),p(t,o,b),w(s,t,b),i=!0},i(t){i||(k(n.$$.fragment,t),k(s.$$.fragment,t),i=!0)},o(t){$(n.$$.fragment,t),$(s.$$.fragment,t),i=!1},d(t){q(n,t),t&&l(o),q(s,t)}}}function pa(j){let n,o,s,i;return n=new E({props:{code:`model = AutoModelForSequenceClassification.from_pretrained(checkpoint)

sequence1_ids = [[200, 200, 200]]
sequence2_ids = [[200, 200]]
batched_ids = [
    [200, 200, 200],
    [200, 200, tokenizer.pad_token_id],
]

print(model(torch.tensor(sequence1_ids)).logits)
print(model(torch.tensor(sequence2_ids)).logits)
print(model(torch.tensor(batched_ids)).logits)`,highlighted:`model = AutoModelForSequenceClassification.from_pretrained(checkpoint)

sequence1_ids = [[<span class="hljs-number">200</span>, <span class="hljs-number">200</span>, <span class="hljs-number">200</span>]]
sequence2_ids = [[<span class="hljs-number">200</span>, <span class="hljs-number">200</span>]]
batched_ids = [
    [<span class="hljs-number">200</span>, <span class="hljs-number">200</span>, <span class="hljs-number">200</span>],
    [<span class="hljs-number">200</span>, <span class="hljs-number">200</span>, tokenizer.pad_token_id],
]

<span class="hljs-built_in">print</span>(model(torch.tensor(sequence1_ids)).logits)
<span class="hljs-built_in">print</span>(model(torch.tensor(sequence2_ids)).logits)
<span class="hljs-built_in">print</span>(model(torch.tensor(batched_ids)).logits)`}}),s=new E({props:{code:`tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward>)
tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)
tensor([[ 1.5694, -1.3895],
        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward>)`,highlighted:`tensor([[ <span class="hljs-number">1.5694</span>, -<span class="hljs-number">1.3895</span>]], grad_fn=&lt;AddmmBackward&gt;)
tensor([[ <span class="hljs-number">0.5803</span>, -<span class="hljs-number">0.4125</span>]], grad_fn=&lt;AddmmBackward&gt;)
tensor([[ <span class="hljs-number">1.5694</span>, -<span class="hljs-number">1.3895</span>],
        [ <span class="hljs-number">1.3373</span>, -<span class="hljs-number">1.2163</span>]], grad_fn=&lt;AddmmBackward&gt;)`}}),{c(){g(n.$$.fragment),o=m(),g(s.$$.fragment)},l(t){v(n.$$.fragment,t),o=_(t),v(s.$$.fragment,t)},m(t,b){w(n,t,b),p(t,o,b),w(s,t,b),i=!0},i(t){i||(k(n.$$.fragment,t),k(s.$$.fragment,t),i=!0)},o(t){$(n.$$.fragment,t),$(s.$$.fragment,t),i=!1},d(t){q(n,t),t&&l(o),q(s,t)}}}function ia(j){let n,o,s,i;return n=new E({props:{code:`batched_ids = [
    [200, 200, 200],
    [200, 200, tokenizer.pad_token_id],
]

attention_mask = [
    [1, 1, 1],
    [1, 1, 0],
]

outputs = model(tf.constant(batched_ids), attention_mask=tf.constant(attention_mask))
print(outputs.logits)`,highlighted:`batched_ids = [
    [<span class="hljs-number">200</span>, <span class="hljs-number">200</span>, <span class="hljs-number">200</span>],
    [<span class="hljs-number">200</span>, <span class="hljs-number">200</span>, tokenizer.pad_token_id],
]

attention_mask = [
    [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
    [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>],
]

outputs = model(tf.constant(batched_ids), attention_mask=tf.constant(attention_mask))
<span class="hljs-built_in">print</span>(outputs.logits)`}}),s=new E({props:{code:`tf.Tensor(
[[ 1.5693681  -1.3894582 ]
 [ 0.5803021  -0.41252586]], shape=(2, 2), dtype=float32)`,highlighted:`tf.Tensor(
[[ <span class="hljs-number">1.5693681</span>  -<span class="hljs-number">1.3894582</span> ]
 [ <span class="hljs-number">0.5803021</span>  -<span class="hljs-number">0.41252586</span>]], shape=(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>), dtype=float32)`}}),{c(){g(n.$$.fragment),o=m(),g(s.$$.fragment)},l(t){v(n.$$.fragment,t),o=_(t),v(s.$$.fragment,t)},m(t,b){w(n,t,b),p(t,o,b),w(s,t,b),i=!0},i(t){i||(k(n.$$.fragment,t),k(s.$$.fragment,t),i=!0)},o(t){$(n.$$.fragment,t),$(s.$$.fragment,t),i=!1},d(t){q(n,t),t&&l(o),q(s,t)}}}function ua(j){let n,o,s,i;return n=new E({props:{code:`batched_ids = [
    [200, 200, 200],
    [200, 200, tokenizer.pad_token_id],
]

attention_mask = [
    [1, 1, 1],
    [1, 1, 0],
]

outputs = model(torch.tensor(batched_ids), attention_mask=torch.tensor(attention_mask))
print(outputs.logits)`,highlighted:`batched_ids = [
    [<span class="hljs-number">200</span>, <span class="hljs-number">200</span>, <span class="hljs-number">200</span>],
    [<span class="hljs-number">200</span>, <span class="hljs-number">200</span>, tokenizer.pad_token_id],
]

attention_mask = [
    [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
    [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>],
]

outputs = model(torch.tensor(batched_ids), attention_mask=torch.tensor(attention_mask))
<span class="hljs-built_in">print</span>(outputs.logits)`}}),s=new E({props:{code:`tensor([[ 1.5694, -1.3895],
        [ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)`,highlighted:`tensor([[ <span class="hljs-number">1.5694</span>, -<span class="hljs-number">1.3895</span>],
        [ <span class="hljs-number">0.5803</span>, -<span class="hljs-number">0.4125</span>]], grad_fn=&lt;AddmmBackward&gt;)`}}),{c(){g(n.$$.fragment),o=m(),g(s.$$.fragment)},l(t){v(n.$$.fragment,t),o=_(t),v(s.$$.fragment,t)},m(t,b){w(n,t,b),p(t,o,b),w(s,t,b),i=!0},i(t){i||(k(n.$$.fragment,t),k(s.$$.fragment,t),i=!0)},o(t){$(n.$$.fragment,t),$(s.$$.fragment,t),i=!1},d(t){q(n,t),t&&l(o),q(s,t)}}}function ca(j){let n,o,s,i,t;return{c(){n=u("p"),o=h("\u270F\uFE0F "),s=u("strong"),i=h("\uC9C1\uC811 \uD574\uBCF4\uC138\uC694!"),t=h(" 2\uC7A5\uC5D0\uC11C \uC0AC\uC6A9\uD55C \uB450 \uAC1C\uC758 \uBB38\uC7A5(\u201CI\u2019ve been waiting for a HuggingFace course my whole life.\u201D and \u201CI hate this so much!\u201C)\uC744 \uC774\uC6A9\uD574 \uC9C1\uC811 \uD1A0\uD070\uD654\uB97C \uC801\uC6A9\uD574\uBCF4\uC138\uC694. \uD1A0\uD070\uD654 \uACB0\uACFC\uB97C \uBAA8\uB378\uC5D0 \uB118\uAE30\uACE0 2\uC7A5\uC5D0\uC11C \uC5BB\uC740 \uAC83\uACFC \uB3D9\uC77C\uD55C \uB85C\uC9D3\uC744 \uC5BB\uC5C8\uB294\uC9C0 \uD655\uC778\uD574\uBCF4\uC138\uC694. \uC774\uC81C Now batch them together using the padding token, then create the proper attention mask. Check that you obtain the same results when going through the model!")},l(b){n=c(b,"P",{});var z=f(n);o=d(z,"\u270F\uFE0F "),s=c(z,"STRONG",{});var A=f(s);i=d(A,"\uC9C1\uC811 \uD574\uBCF4\uC138\uC694!"),A.forEach(l),t=d(z," 2\uC7A5\uC5D0\uC11C \uC0AC\uC6A9\uD55C \uB450 \uAC1C\uC758 \uBB38\uC7A5(\u201CI\u2019ve been waiting for a HuggingFace course my whole life.\u201D and \u201CI hate this so much!\u201C)\uC744 \uC774\uC6A9\uD574 \uC9C1\uC811 \uD1A0\uD070\uD654\uB97C \uC801\uC6A9\uD574\uBCF4\uC138\uC694. \uD1A0\uD070\uD654 \uACB0\uACFC\uB97C \uBAA8\uB378\uC5D0 \uB118\uAE30\uACE0 2\uC7A5\uC5D0\uC11C \uC5BB\uC740 \uAC83\uACFC \uB3D9\uC77C\uD55C \uB85C\uC9D3\uC744 \uC5BB\uC5C8\uB294\uC9C0 \uD655\uC778\uD574\uBCF4\uC138\uC694. \uC774\uC81C Now batch them together using the padding token, then create the proper attention mask. Check that you obtain the same results when going through the model!"),z.forEach(l)},m(b,z){p(b,n,z),a(n,o),a(n,s),a(s,i),a(n,t)},d(b){b&&l(n)}}}function fa(j){let n,o,s,i,t,b,z,A,K,I,Ne,_e,P,S,xe,C,M,Le,He,Ft,Bs,T,_s,Pt,St,Be,ds,Ct,Mt,Dt,bs,Nt,xt,$s,Lt,Os,Oe,Ht,Rs,V,se,ks,de,Bt,js,Ot,Gs,Re,Rt,Us,D,N,Ge,Ue,Gt,Ys,te,Ut,gs,Yt,Jt,Js,x,L,Ye,Je,Qt,Qs,H,B,Qe,Ke,Kt,Ks,O,R,Ve,be,vs,Vt,Wt,Vs,$e,Ws,We,Xt,Xs,ne,Zs,le,Zt,ws,en,sn,et,W,ae,qs,ke,tn,ys,nn,st,Xe,ln,tt,je,nt,X,Es,an,rn,zs,on,pn,lt,ge,at,re,un,As,cn,fn,rt,G,U,Ze,es,mn,ot,oe,hn,Is,_n,dn,pt,Z,pe,Ts,ve,bn,Fs,$n,it,we,Ps,kn,jn,ut,ss,gn,ct,Y,J,ts,ns,vn,ft,ls,wn,mt,ie,ht,ee,ue,Ss,qe,qn,Cs,yn,_t,as,En,dt,ce,Ms,zn,An,Ds,In,bt,Q,Tn,ye,Fn,Pn,Ee,Sn,Cn,$t,fe,Mn,Ns,Dn,Nn,kt,ze,jt;s=new Jl({props:{fw:j[0]}}),A=new Hs({});const Hn=[Kl,Ql],Ae=[];function Bn(e,r){return e[0]==="pt"?0:1}P=Bn(j),S=Ae[P]=Hn[P](j);const On=[Wl,Vl],Ie=[];function Rn(e,r){return e[0]==="pt"?0:1}C=Rn(j),M=Ie[C]=On[C](j),de=new Hs({});const Gn=[Zl,Xl],Te=[];function Un(e,r){return e[0]==="pt"?0:1}D=Un(j),N=Te[D]=Gn[D](j);const Yn=[sa,ea],Fe=[];function Jn(e,r){return e[0]==="pt"?0:1}x=Jn(j),L=Fe[x]=Yn[x](j);const Qn=[na,ta],Pe=[];function Kn(e,r){return e[0]==="pt"?0:1}H=Kn(j),B=Pe[H]=Qn[H](j);const Vn=[aa,la],Se=[];function Wn(e,r){return e[0]==="pt"?0:1}O=Wn(j),R=Se[O]=Vn[O](j),$e=new E({props:{code:"batched_ids = [ids, ids]",highlighted:'<span class="hljs-attr">batched_ids</span> = [ids, ids]'}}),ne=new Ll({props:{$$slots:{default:[ra]},$$scope:{ctx:j}}}),ke=new Hs({}),je=new E({props:{code:`batched_ids = [
    [200, 200, 200],
    [200, 200]
]`,highlighted:`batched_ids = [
    [<span class="hljs-number">200</span>, <span class="hljs-number">200</span>, <span class="hljs-number">200</span>],
    [<span class="hljs-number">200</span>, <span class="hljs-number">200</span>]
]`}}),ge=new E({props:{code:`padding_id = 100

batched_ids = [
    [200, 200, 200],
    [200, 200, padding_id],
]`,highlighted:`padding_id = <span class="hljs-number">100</span>

batched_ids = [
    [<span class="hljs-number">200</span>, <span class="hljs-number">200</span>, <span class="hljs-number">200</span>],
    [<span class="hljs-number">200</span>, <span class="hljs-number">200</span>, padding_id],
]`}});const Xn=[pa,oa],Ce=[];function Zn(e,r){return e[0]==="pt"?0:1}G=Zn(j),U=Ce[G]=Xn[G](j),ve=new Hs({});const el=[ua,ia],Me=[];function sl(e,r){return e[0]==="pt"?0:1}return Y=sl(j),J=Me[Y]=el[Y](j),ie=new Ll({props:{$$slots:{default:[ca]},$$scope:{ctx:j}}}),qe=new Hs({}),ze=new E({props:{code:"sequence = sequence[:max_sequence_length]",highlighted:"sequence = sequence[:max_sequence_length]"}}),{c(){n=u("meta"),o=m(),g(s.$$.fragment),i=m(),t=u("h1"),b=u("a"),z=u("span"),g(A.$$.fragment),K=m(),I=u("span"),Ne=h("\uB2E4\uC911 \uC2DC\uD000\uC2A4 \uCC98\uB9AC"),_e=m(),S.c(),xe=m(),M.c(),Le=m(),He=u("p"),Ft=h("\uC774\uC804 \uC139\uC158\uC5D0\uC11C, \uC6B0\uB9AC\uB294 \uAC00\uC7A5 \uAC04\uB2E8\uD55C \uC0AC\uC6A9 \uC608\uC2DC\uB97C \uD655\uC778\uD588\uC2B5\uB2C8\uB2E4. \uAE38\uC774\uAC00 \uC9E7\uC740 \uB2E8\uC77C \uC2DC\uD000\uC2A4\uC5D0 \uB300\uD55C \uCD94\uB860\uC744 \uC218\uD589\uD588\uC2B5\uB2C8\uB2E4. \uADF8\uB7EC\uB098 \uC774\uBBF8 \uBA87 \uAC00\uC9C0 \uAD81\uAE08\uC99D\uC774 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