import{S as Us,i as Ms,s as Ns,e as n,k as p,w as y,t as r,M as $s,c as a,d as o,m as h,a as s,x as J,h as l,b as i,G as e,g,y as v,q as w,o as k,B as U,v as Gs,L as Jo}from"../../chunks/vendor-hf-doc-builder.js";import{T as ks}from"../../chunks/Tip-hf-doc-builder.js";import{D as He}from"../../chunks/Docstring-hf-doc-builder.js";import{C as At}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Te}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as yo}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function js(j){let c,T;return c=new At({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> GPTNeoXJapaneseConfig, GPTNeoXJapaneseModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a GPTNeoXJapanese gpt-neox-japanese-2.7b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = GPTNeoXJapaneseConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the gpt-neox-japanese-2.7b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = GPTNeoXJapaneseModel(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(){y(c.$$.fragment)},l(u){J(c.$$.fragment,u)},m(u,f){v(c,u,f),T=!0},p:Jo,i(u){T||(w(c.$$.fragment,u),T=!0)},o(u){k(c.$$.fragment,u),T=!1},d(u){U(c,u)}}}function Es(j){let c,T,u,f,b;return f=new At({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> GPTNeoXJapaneseTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = GPTNeoXJapaneseTokenizer.from_pretrained(<span class="hljs-string">&quot;abeja/gpt-neox-japanese-2.7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># You can confirm both \u6176\u5FDC and \u6176\u61C9 are encoded to 17749</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer(<span class="hljs-string">&quot;\u543E\u8F29\u306F\u732B\u3067\u3042\u308B\u{1F42F}\u3002\u5B9F\u306F\u6176\u5FDC(\u6176\u61C9)\u5927\u5B66\u51FA\u8EAB&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
[<span class="hljs-number">30014</span>, <span class="hljs-number">26883</span>, <span class="hljs-number">26638</span>, <span class="hljs-number">27228</span>, <span class="hljs-number">25</span>, <span class="hljs-number">26650</span>, <span class="hljs-number">31732</span>, <span class="hljs-number">31679</span>, <span class="hljs-number">27809</span>, <span class="hljs-number">26638</span>, <span class="hljs-number">17749</span>, <span class="hljs-number">31592</span>, <span class="hljs-number">17749</span>, <span class="hljs-number">31593</span>, <span class="hljs-number">321</span>, <span class="hljs-number">1281</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Both \u6176\u5FDC and \u6176\u61C9 are decoded to \u6176\u5FDC</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(tokenizer(<span class="hljs-string">&quot;\u543E\u8F29\u306F\u732B\u3067\u3042\u308B\u{1F42F}\u3002\u5B9F\u306F\u6176\u5FDC(\u6176\u61C9)\u5927\u5B66\u51FA\u8EAB&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-string">&#x27;\u543E\u8F29\u306F\u732B\u3067\u3042\u308B\u{1F42F}\u3002\u5B9F\u306F\u6176\u5FDC(\u6176\u5FDC)\u5927\u5B66\u51FA\u8EAB&#x27;</span>`}}),{c(){c=n("p"),T=r("Example:"),u=p(),y(f.$$.fragment)},l(d){c=a(d,"P",{});var _=s(c);T=l(_,"Example:"),_.forEach(o),u=h(d),J(f.$$.fragment,d)},m(d,_){g(d,c,_),e(c,T),g(d,u,_),v(f,d,_),b=!0},p:Jo,i(d){b||(w(f.$$.fragment,d),b=!0)},o(d){k(f.$$.fragment,d),b=!1},d(d){d&&o(c),d&&o(u),U(f,d)}}}function Ps(j){let c,T,u,f,b;return{c(){c=n("p"),T=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=n("code"),f=r("Module"),b=r(`
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(d){c=a(d,"P",{});var _=s(c);T=l(_,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a(_,"CODE",{});var F=s(u);f=l(F,"Module"),F.forEach(o),b=l(_,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),_.forEach(o)},m(d,_){g(d,c,_),e(c,T),e(c,u),e(u,f),e(c,b)},d(d){d&&o(c)}}}function Xs(j){let c,T,u,f,b;return f=new At({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, GPTNeoXJapaneseModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;abeja/gpt-neox-japanese-2.7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = GPTNeoXJapaneseModel.from_pretrained(<span class="hljs-string">&quot;abeja/gpt-neox-japanese-2.7b&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;\u65E5\u672C\u8A9E\u306EGPT-neox\u304CHugging Face\u3067\u4F7F\u3048\u307E\u3059\u{1F600}&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=n("p"),T=r("Example:"),u=p(),y(f.$$.fragment)},l(d){c=a(d,"P",{});var _=s(c);T=l(_,"Example:"),_.forEach(o),u=h(d),J(f.$$.fragment,d)},m(d,_){g(d,c,_),e(c,T),g(d,u,_),v(f,d,_),b=!0},p:Jo,i(d){b||(w(f.$$.fragment,d),b=!0)},o(d){k(f.$$.fragment,d),b=!1},d(d){d&&o(c),d&&o(u),U(f,d)}}}function Cs(j){let c,T,u,f,b;return{c(){c=n("p"),T=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=n("code"),f=r("Module"),b=r(`
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(d){c=a(d,"P",{});var _=s(c);T=l(_,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a(_,"CODE",{});var F=s(u);f=l(F,"Module"),F.forEach(o),b=l(_,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),_.forEach(o)},m(d,_){g(d,c,_),e(c,T),e(c,u),e(u,f),e(c,b)},d(d){d&&o(c)}}}function zs(j){let c,T,u,f,b;return f=new At({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseConfig
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;abeja/gpt-neox-japanese-2.7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config = GPTNeoXJapaneseConfig.from_pretrained(<span class="hljs-string">&quot;abeja/gpt-neox-japanese-2.7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config.is_decoder = <span class="hljs-literal">True</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = GPTNeoXJapaneseForCausalLM.from_pretrained(<span class="hljs-string">&quot;abeja/gpt-neox-japanese-2.7b&quot;</span>, config=config)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;\u65E5\u672C\u8A9E\u306EGPT-neox\u304CHugging Face\u3067\u4F7F\u3048\u307E\u3059\u{1F600}&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>prediction_logits = outputs.logits`}}),{c(){c=n("p"),T=r("Example:"),u=p(),y(f.$$.fragment)},l(d){c=a(d,"P",{});var _=s(c);T=l(_,"Example:"),_.forEach(o),u=h(d),J(f.$$.fragment,d)},m(d,_){g(d,c,_),e(c,T),g(d,u,_),v(f,d,_),b=!0},p:Jo,i(d){b||(w(f.$$.fragment,d),b=!0)},o(d){k(f.$$.fragment,d),b=!1},d(d){d&&o(c),d&&o(u),U(f,d)}}}function xs(j){let c,T,u,f,b,d,_,F,vo,Lt,W,ee,ut,be,wo,ft,ko,Qt,N,Uo,ye,Mo,No,Je,$o,Go,gt,jo,Eo,ve,Po,Xo,we,Co,zo,Ot,M,xo,ke,Fo,Vo,Ue,Zo,Io,Me,Wo,Bo,Ne,So,Ro,$e,qo,Ao,Ge,Lo,Qo,Yt,B,te,_t,je,Oo,Tt,Yo,Ht,oe,Ho,bt,Do,Ko,Dt,Ee,Kt,S,ne,yt,Pe,en,Jt,tn,eo,De,vt,Ke,on,to,R,ae,wt,Xe,nn,kt,an,oo,P,Ce,sn,q,rn,Ut,ln,dn,ze,cn,pn,hn,A,mn,et,un,fn,tt,gn,_n,Tn,se,no,L,re,Mt,xe,bn,Nt,yn,ao,$,Fe,Jn,Q,vn,ot,wn,kn,Ve,Un,Mn,Nn,G,$t,$n,Gn,Gt,jn,En,nt,Pn,jt,Xn,Cn,Et,zn,xn,Pt,Fn,Vn,Xt,Zn,In,le,Wn,ie,Ze,Bn,Ct,Sn,so,O,de,zt,Ie,Rn,xt,qn,ro,V,We,An,Be,Ln,Se,Qn,On,Yn,X,Re,Hn,Y,Dn,at,Kn,ea,Ft,ta,oa,na,ce,aa,pe,lo,H,he,Vt,qe,sa,Zt,ra,io,Z,Ae,la,D,ia,It,da,ca,Le,pa,ha,ma,C,Qe,ua,K,fa,st,ga,_a,Wt,Ta,ba,ya,me,Ja,ue,co;return d=new Te({}),be=new Te({}),je=new Te({}),Ee=new At({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEdQVE5lb1hKYXBhbmVzZUZvckNhdXNhbExNJTJDJTIwR1BUTmVvWEphcGFuZXNlVG9rZW5pemVyJTBBJTBBbW9kZWwlMjAlM0QlMjBHUFROZW9YSmFwYW5lc2VGb3JDYXVzYWxMTS5mcm9tX3ByZXRyYWluZWQoJTIyYWJlamElMkZncHQtbmVveC1qYXBhbmVzZS0yLjdiJTIyKSUwQXRva2VuaXplciUyMCUzRCUyMEdQVE5lb1hKYXBhbmVzZVRva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyYWJlamElMkZncHQtbmVveC1qYXBhbmVzZS0yLjdiJTIyKSUwQSUwQXByb21wdCUyMCUzRCUyMCUyMiVFNCVCQSVCQSVFMyU4MSVBOEFJJUUzJTgxJThDJUU1JThEJTk0JUU4JUFBJUJGJUUzJTgxJTk5JUUzJTgyJThCJUUzJTgxJTlGJUUzJTgyJTgxJUUzJTgxJUFCJUUzJTgxJUFGJUUzJTgwJTgxJTIyJTBBJTBBaW5wdXRfaWRzJTIwJTNEJTIwdG9rZW5pemVyKHByb21wdCUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpLmlucHV0X2lkcyUwQSUwQWdlbl90b2tlbnMlMjAlM0QlMjBtb2RlbC5nZW5lcmF0ZSglMEElMjAlMjAlMjAlMjBpbnB1dF9pZHMlMkMlMEElMjAlMjAlMjAlMjBkb19zYW1wbGUlM0RUcnVlJTJDJTBBJTIwJTIwJTIwJTIwdGVtcGVyYXR1cmUlM0QwLjklMkMlMEElMjAlMjAlMjAlMjBtYXhfbGVuZ3RoJTNEMTAwJTJDJTBBKSUwQWdlbl90ZXh0JTIwJTNEJTIwdG9rZW5pemVyLmJhdGNoX2RlY29kZShnZW5fdG9rZW5zJTJDJTIwc2tpcF9zcGVjaWFsX3Rva2VucyUzRFRydWUpJTVCMCU1RCUwQSUwQXByaW50KGdlbl90ZXh0KQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = GPTNeoXJapaneseForCausalLM.from_pretrained(<span class="hljs-string">&quot;abeja/gpt-neox-japanese-2.7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = GPTNeoXJapaneseTokenizer.from_pretrained(<span class="hljs-string">&quot;abeja/gpt-neox-japanese-2.7b&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;\u4EBA\u3068AI\u304C\u5354\u8ABF\u3059\u308B\u305F\u3081\u306B\u306F\u3001&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(prompt, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_ids

<span class="hljs-meta">&gt;&gt;&gt; </span>gen_tokens = model.generate(
<span class="hljs-meta">... </span>    input_ids,
<span class="hljs-meta">... </span>    do_sample=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    temperature=<span class="hljs-number">0.9</span>,
<span class="hljs-meta">... </span>    max_length=<span class="hljs-number">100</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>gen_text = tokenizer.batch_decode(gen_tokens, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(gen_text)
\u4EBA\u3068AI\u304C\u5354\u8ABF\u3059\u308B\u305F\u3081\u306B\u306F\u3001AI\u3068\u4EBA\u304C\u5171\u5B58\u3057\u3001AI\u3092\u6B63\u3057\u304F\u7406\u89E3\u3059\u308B\u5FC5\u8981\u304C\u3042\u308A\u307E\u3059\u3002`}}),Pe=new Te({}),Xe=new Te({}),Ce=new He({props:{name:"class transformers.GPTNeoXJapaneseConfig",anchor:"transformers.GPTNeoXJapaneseConfig",parameters:[{name:"vocab_size",val:" = 32000"},{name:"hidden_size",val:" = 2560"},{name:"num_hidden_layers",val:" = 32"},{name:"num_attention_heads",val:" = 32"},{name:"intermediate_multiple_size",val:" = 4"},{name:"hidden_act",val:" = 'gelu'"},{name:"rotary_pct",val:" = 1.0"},{name:"rotary_emb_base",val:" = 10000"},{name:"max_position_embeddings",val:" = 2048"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"use_cache",val:" = True"},{name:"bos_token_id",val:" = 31996"},{name:"eos_token_id",val:" = 31999"},{name:"attention_dropout",val:" = 0.1"},{name:"hidden_dropout",val:" = 0.0"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.GPTNeoXJapaneseConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 32000) &#x2014;
Vocabulary size of the GPTNeoXJapanese model. Defines the number of different tokens that can be
represented by the <code>inputs_ids</code> passed when calling <code>GPTNeoXJapanese</code>.`,name:"vocab_size"},{anchor:"transformers.GPTNeoXJapaneseConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2560) &#x2014;
Dimension of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.GPTNeoXJapaneseConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.GPTNeoXJapaneseConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.GPTNeoXJapaneseConfig.intermediate_multiple_size",description:`<strong>intermediate_multiple_size</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
Dimension of the &#x201C;intermediate&#x201D; layer in the Transformer encoder is calculated by hidden_size *
intermediate_multiple_size.`,name:"intermediate_multiple_size"},{anchor:"transformers.GPTNeoXJapaneseConfig.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.`,name:"hidden_act"},{anchor:"transformers.GPTNeoXJapaneseConfig.rotary_pct",description:`<strong>rotary_pct</strong> (<code>float</code>, <em>optional</em>, defaults to 1.00) &#x2014;
percentage of hidden dimensions to allocate to rotary embeddings`,name:"rotary_pct"},{anchor:"transformers.GPTNeoXJapaneseConfig.rotary_emb_base",description:`<strong>rotary_emb_base</strong> (<code>int</code>, <em>optional</em>, defaults to 10000) &#x2014;
base for computing rotary embeddings frequency`,name:"rotary_emb_base"},{anchor:"transformers.GPTNeoXJapaneseConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
The maximum sequence length that this model might ever be used with.`,name:"max_position_embeddings"},{anchor:"transformers.GPTNeoXJapaneseConfig.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.GPTNeoXJapaneseConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-5) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.GPTNeoXJapaneseConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if <code>config.is_decoder=True</code>.`,name:"use_cache"},{anchor:"transformers.GPTNeoXJapaneseConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention.`,name:"attention_dropout"},{anchor:"transformers.GPTNeoXJapaneseConfig.hidden_dropout",description:`<strong>hidden_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the hidden layer.
Example &#x2014;`,name:"hidden_dropout"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py#L28"}}),se=new yo({props:{anchor:"transformers.GPTNeoXJapaneseConfig.example",$$slots:{default:[js]},$$scope:{ctx:j}}}),xe=new Te({}),Fe=new He({props:{name:"class transformers.GPTNeoXJapaneseTokenizer",anchor:"transformers.GPTNeoXJapaneseTokenizer",parameters:[{name:"vocab_file",val:""},{name:"emoji_file",val:""},{name:"unk_token",val:" = '<|endoftext|>'"},{name:"pad_token",val:" = '<|endoftext|>'"},{name:"bos_token",val:" = '<|startoftext|>'"},{name:"eos_token",val:" = '<|endoftext|>'"},{name:"do_clean_text",val:" = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.GPTNeoXJapaneseTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
File containing the vocabulary.`,name:"vocab_file"},{anchor:"transformers.GPTNeoXJapaneseTokenizer.emoji_file",description:`<strong>emoji_file</strong> (<code>str</code>) &#x2014;
File containing the emoji.`,name:"emoji_file"},{anchor:"transformers.GPTNeoXJapaneseTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|endoftext|&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.GPTNeoXJapaneseTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|endoftext|&gt;&quot;</code>) &#x2014;
The token used for padding`,name:"pad_token"},{anchor:"transformers.GPTNeoXJapaneseTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|startoftext|&gt;&quot;</code>) &#x2014;
The beginning of sequence token.`,name:"bos_token"},{anchor:"transformers.GPTNeoXJapaneseTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|endoftext|&gt;&quot;</code>) &#x2014;
The end of sequence token.`,name:"eos_token"},{anchor:"transformers.GPTNeoXJapaneseTokenizer.do_clean_text",description:`<strong>do_clean_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to clean text for URL, EMAIL, TEL, Japanese DATE and Japanese PRICE.`,name:"do_clean_text"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py#L70"}}),le=new yo({props:{anchor:"transformers.GPTNeoXJapaneseTokenizer.example",$$slots:{default:[Es]},$$scope:{ctx:j}}}),Ze=new He({props:{name:"convert_tokens_to_string",anchor:"transformers.GPTNeoXJapaneseTokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py#L177"}}),Ie=new Te({}),We=new He({props:{name:"class transformers.GPTNeoXJapaneseModel",anchor:"transformers.GPTNeoXJapaneseModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.GPTNeoXJapaneseModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">~GPTNeoXJapaneseConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py#L440"}}),Re=new He({props:{name:"forward",anchor:"transformers.GPTNeoXJapaneseModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.GPTNeoXJapaneseModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>.`,name:"input_ids"},{anchor:"transformers.GPTNeoXJapaneseModel.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>`,name:"attention_mask"},{anchor:"transformers.GPTNeoXJapaneseModel.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>`,name:"token_type_ids"},{anchor:"transformers.GPTNeoXJapaneseModel.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>.`,name:"position_ids"},{anchor:"transformers.GPTNeoXJapaneseModel.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.GPTNeoXJapaneseModel.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.GPTNeoXJapaneseModel.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.GPTNeoXJapaneseModel.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.GPTNeoXJapaneseModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.GPTNeoXJapaneseModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.GPTNeoXJapaneseModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py#L460",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast"
>transformers.modeling_outputs.BaseModelOutputWithPast</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig"
>GPTNeoXJapaneseConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and optionally if
<code>config.is_encoder_decoder=True</code> 2 additional tensors of shape <code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
<code>config.is_encoder_decoder=True</code> in the cross-attention blocks) that can be used (see <code>past_key_values</code>
input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast"
>transformers.modeling_outputs.BaseModelOutputWithPast</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ce=new ks({props:{$$slots:{default:[Ps]},$$scope:{ctx:j}}}),pe=new yo({props:{anchor:"transformers.GPTNeoXJapaneseModel.forward.example",$$slots:{default:[Xs]},$$scope:{ctx:j}}}),qe=new Te({}),Ae=new He({props:{name:"class transformers.GPTNeoXJapaneseForCausalLM",anchor:"transformers.GPTNeoXJapaneseForCausalLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.GPTNeoXJapaneseForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">~GPTNeoXJapaneseConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py#L595"}}),Qe=new He({props:{name:"forward",anchor:"transformers.GPTNeoXJapaneseForCausalLM.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.GPTNeoXJapaneseForCausalLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>.`,name:"input_ids"},{anchor:"transformers.GPTNeoXJapaneseForCausalLM.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>`,name:"attention_mask"},{anchor:"transformers.GPTNeoXJapaneseForCausalLM.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>`,name:"token_type_ids"},{anchor:"transformers.GPTNeoXJapaneseForCausalLM.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>.`,name:"position_ids"},{anchor:"transformers.GPTNeoXJapaneseForCausalLM.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.GPTNeoXJapaneseForCausalLM.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.GPTNeoXJapaneseForCausalLM.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.GPTNeoXJapaneseForCausalLM.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.GPTNeoXJapaneseForCausalLM.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.GPTNeoXJapaneseForCausalLM.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) &#x2014;
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>. The two additional tensors are
only required when the model is used as a decoder in a Sequence to Sequence model.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.GPTNeoXJapaneseForCausalLM.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
<code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are
ignored (masked), the loss is only computed for the tokens with labels n <code>[0, ..., config.vocab_size]</code>.`,name:"labels"},{anchor:"transformers.GPTNeoXJapaneseForCausalLM.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py#L614",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast"
>transformers.modeling_outputs.CausalLMOutputWithPast</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig"
>GPTNeoXJapaneseConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Language modeling loss (for next-token prediction).</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>)</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast"
>transformers.modeling_outputs.CausalLMOutputWithPast</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),me=new ks({props:{$$slots:{default:[Cs]},$$scope:{ctx:j}}}),ue=new yo({props:{anchor:"transformers.GPTNeoXJapaneseForCausalLM.forward.example",$$slots:{default:[zs]},$$scope:{ctx:j}}}),{c(){c=n("meta"),T=p(),u=n("h1"),f=n("a"),b=n("span"),y(d.$$.fragment),_=p(),F=n("span"),vo=r("GPT-NeoX-Japanese"),Lt=p(),W=n("h2"),ee=n("a"),ut=n("span"),y(be.$$.fragment),wo=p(),ft=n("span"),ko=r("Overview"),Qt=p(),N=n("p"),Uo=r("We introduce GPT-NeoX-Japanese, which is an autoregressive language model for Japanese, trained on top of "),ye=n("a"),Mo=r("https://github.com/EleutherAI/gpt-neox"),No=r(`.
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