import{s as Zn,o as Wn,n as qe}from"../chunks/scheduler.9bc65507.js";import{S as In,i as Bn,g as o,s as i,u as g,A as Sn,h as a,f as t,c as d,j as u,v as f,y as v,k as h,o as n,a as c,w as _,d as T,t as b,x as y}from"../chunks/index.78c82d43.js";import{T as En}from"../chunks/Tip.87d55b76.js";import{D as ke}from"../chunks/Docstring.f3d04bd2.js";import{C as st}from"../chunks/CodeBlock.48618ec2.js";import{I as oe}from"../chunks/IconCopyLink.bedaa44d.js";import{E as jt}from"../chunks/ExampleCodeBlock.872b014d.js";function Rn(k){let s,J;return s=new st({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEdQVE5lb1hKYXBhbmVzZUNvbmZpZyUyQyUyMEdQVE5lb1hKYXBhbmVzZU1vZGVsJTBBJTBBJTIzJTIwSW5pdGlhbGl6aW5nJTIwYSUyMEdQVE5lb1hKYXBhbmVzZSUyMGdwdC1uZW94LWphcGFuZXNlLTIuN2IlMjBzdHlsZSUyMGNvbmZpZ3VyYXRpb24lMEFjb25maWd1cmF0aW9uJTIwJTNEJTIwR1BUTmVvWEphcGFuZXNlQ29uZmlnKCklMEElMEElMjMlMjBJbml0aWFsaXppbmclMjBhJTIwbW9kZWwlMjAod2l0aCUyMHJhbmRvbSUyMHdlaWdodHMpJTIwZnJvbSUyMHRoZSUyMGdwdC1uZW94LWphcGFuZXNlLTIuN2IlMjBzdHlsZSUyMGNvbmZpZ3VyYXRpb24lMEFtb2RlbCUyMCUzRCUyMEdQVE5lb1hKYXBhbmVzZU1vZGVsKGNvbmZpZ3VyYXRpb24pJTBBJTBBJTIzJTIwQWNjZXNzaW5nJTIwdGhlJTIwbW9kZWwlMjBjb25maWd1cmF0aW9uJTBBY29uZmlndXJhdGlvbiUyMCUzRCUyMG1vZGVsLmNvbmZpZw==",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(){g(s.$$.fragment)},l(p){f(s.$$.fragment,p)},m(p,m){_(s,p,m),J=!0},p:qe,i(p){J||(T(s.$$.fragment,p),J=!0)},o(p){b(s.$$.fragment,p),J=!1},d(p){y(s,p)}}}function Ln(k){let s,J="Example:",p,m,M;return m=new st({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEdQVE5lb1hKYXBhbmVzZVRva2VuaXplciUwQSUwQXRva2VuaXplciUyMCUzRCUyMEdQVE5lb1hKYXBhbmVzZVRva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyYWJlamElMkZncHQtbmVveC1qYXBhbmVzZS0yLjdiJTIyKSUwQSUyMyUyMFlvdSUyMGNhbiUyMGNvbmZpcm0lMjBib3RoJTIwJUU2JTg1JUI2JUU1JUJGJTlDJTIwYW5kJTIwJUU2JTg1JUI2JUU2JTg3JTg5JTIwYXJlJTIwZW5jb2RlZCUyMHRvJTIwMTc3NDklMEF0b2tlbml6ZXIoJTIyJUU1JTkwJUJFJUU4JUJDJUE5JUUzJTgxJUFGJUU3JThDJUFCJUUzJTgxJUE3JUUzJTgxJTgyJUUzJTgyJThCJUYwJTlGJTkwJUFGJUUzJTgwJTgyJUU1JUFFJTlGJUUzJTgxJUFGJUU2JTg1JUI2JUU1JUJGJTlDKCVFNiU4NSVCNiVFNiU4NyU4OSklRTUlQTQlQTclRTUlQUQlQTYlRTUlODclQkElRTglQkElQUIlMjIpJTVCJTIyaW5wdXRfaWRzJTIyJTVEJTBBJTBBJTIzJTIwQm90aCUyMCVFNiU4NSVCNiVFNSVCRiU5QyUyMGFuZCUyMCVFNiU4NSVCNiVFNiU4NyU4OSUyMGFyZSUyMGRlY29kZWQlMjB0byUyMCVFNiU4NSVCNiVFNSVCRiU5QyUwQXRva2VuaXplci5kZWNvZGUodG9rZW5pemVyKCUyMiVFNSU5MCVCRSVFOCVCQyVBOSVFMyU4MSVBRiVFNyU4QyVBQiVFMyU4MSVBNyVFMyU4MSU4MiVFMyU4MiU4QiVGMCU5RiU5MCVBRiVFMyU4MCU4MiVFNSVBRSU5RiVFMyU4MSVBRiVFNiU4NSVCNiVFNSVCRiU5QyglRTYlODUlQjYlRTYlODclODkpJUU1JUE0JUE3JUU1JUFEJUE2JUU1JTg3JUJBJUU4JUJBJUFCJTIyKSU1QiUyMmlucHV0X2lkcyUyMiU1RCk=",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 慶応 and 慶應 are encoded to 17749</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer(<span class="hljs-string">&quot;吾輩は猫である🐯。実は慶応(慶應)大学出身&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 慶応 and 慶應 are decoded to 慶応</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(tokenizer(<span class="hljs-string">&quot;吾輩は猫である🐯。実は慶応(慶應)大学出身&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-string">&#x27;吾輩は猫である🐯。実は慶応(慶応)大学出身&#x27;</span>`}}),{c(){s=o("p"),s.textContent=J,p=i(),g(m.$$.fragment)},l(l){s=a(l,"P",{"data-svelte-h":!0}),v(s)!=="svelte-11lpom8"&&(s.textContent=J),p=d(l),f(m.$$.fragment,l)},m(l,w){c(l,s,w),c(l,p,w),_(m,l,w),M=!0},p:qe,i(l){M||(T(m.$$.fragment,l),M=!0)},o(l){b(m.$$.fragment,l),M=!1},d(l){l&&(t(s),t(p)),y(m,l)}}}function qn(k){let s,J=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){s=o("p"),s.innerHTML=J},l(p){s=a(p,"P",{"data-svelte-h":!0}),v(s)!=="svelte-fincs2"&&(s.innerHTML=J)},m(p,m){c(p,s,m)},p:qe,d(p){p&&t(s)}}}function Hn(k){let s,J="Example:",p,m,M;return m=new st({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;日本語のGPT-neoxがHugging Faceで使えます😀&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(){s=o("p"),s.textContent=J,p=i(),g(m.$$.fragment)},l(l){s=a(l,"P",{"data-svelte-h":!0}),v(s)!=="svelte-11lpom8"&&(s.textContent=J),p=d(l),f(m.$$.fragment,l)},m(l,w){c(l,s,w),c(l,p,w),_(m,l,w),M=!0},p:qe,i(l){M||(T(m.$$.fragment,l),M=!0)},o(l){b(m.$$.fragment,l),M=!1},d(l){l&&(t(s),t(p)),y(m,l)}}}function Qn(k){let s,J=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){s=o("p"),s.innerHTML=J},l(p){s=a(p,"P",{"data-svelte-h":!0}),v(s)!=="svelte-fincs2"&&(s.innerHTML=J)},m(p,m){c(p,s,m)},p:qe,d(p){p&&t(s)}}}function An(k){let s,J="Example:",p,m,M;return m=new st({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;日本語のGPT-neoxがHugging Faceで使えます😀&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(){s=o("p"),s.textContent=J,p=i(),g(m.$$.fragment)},l(l){s=a(l,"P",{"data-svelte-h":!0}),v(s)!=="svelte-11lpom8"&&(s.textContent=J),p=d(l),f(m.$$.fragment,l)},m(l,w){c(l,s,w),c(l,p,w),_(m,l,w),M=!0},p:qe,i(l){M||(T(m.$$.fragment,l),M=!0)},o(l){b(m.$$.fragment,l),M=!1},d(l){l&&(t(s),t(p)),y(m,l)}}}function Yn(k){let s,J,p,m,M,l,w,ae,Gt,$e,an="GPT-NeoX-Japanese",rt,X,W,He,se,Xt,Ce,sn="Overview",lt,re,rn=`We introduce GPT-NeoX-Japanese, which is an autoregressive language model for Japanese, trained on top of <a href="https://github.com/EleutherAI/gpt-neox" rel="nofollow">https://github.com/EleutherAI/gpt-neox</a>.
Japanese is a unique language with its large vocabulary and a combination of hiragana, katakana, and kanji writing scripts.
To address this distinct structure of the Japanese language, we use a <a href="https://github.com/tanreinama/Japanese-BPEEncoder_V2" rel="nofollow">special sub-word tokenizer</a>. We are very grateful to <em>tanreinama</em> for open-sourcing this incredibly helpful tokenizer.
Following the recommendations from Google’s research on <a href="https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html" rel="nofollow">PaLM</a>, we have removed bias parameters from transformer blocks, achieving better model performance. Please refer <a href="https://medium.com/ml-abeja/training-a-better-gpt-2-93b157662ae4" rel="nofollow">this article</a> in detail.`,it,le,ln='Development of the model was led by <a href="https://github.com/SO0529" rel="nofollow">Shinya Otani</a>, <a href="https://github.com/spider-man-tm" rel="nofollow">Takayoshi Makabe</a>, <a href="https://github.com/Anuj040" rel="nofollow">Anuj Arora</a>, and <a href="https://github.com/go5paopao" rel="nofollow">Kyo Hattori</a> from <a href="https://www.abejainc.com/" rel="nofollow">ABEJA, Inc.</a>. For more information on this model-building activity, please refer <a href="https://tech-blog.abeja.asia/entry/abeja-gpt-project-202207" rel="nofollow">here (ja)</a>.',dt,z,I,Qe,ie,zt,Ne,dn="Generation",ct,de,cn="The <code>generate()</code> method can be used to generate text using GPT NeoX Japanese model.",pt,ce,ht,P,B,Ae,pe,Pt,xe,pn="Documentation resources",mt,he,hn='<li><a href="../tasks/language_modeling">Causal language modeling task guide</a></li>',ut,V,S,Ye,me,Vt,je,mn="GPTNeoXJapaneseConfig",gt,$,ue,Ft,Ge,un=`This is the configuration class to store the configuration of a <code>GPTNeoXModelJapanese</code>. It is used to instantiate
a GPTNeoX model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the GPTNeoXJapanese
<a href="https://huggingface.co/abeja/gpt-neox-japanese-2.7b" rel="nofollow">abeja/gpt-neox-japanese-2.7b</a> architecture.`,Et,Xe,gn=`Configuration objects inherit from <a href="/docs/transformers/v4.33.3/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/v4.33.3/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information. Default configs is set as 2.7B model`,Zt,R,ft,F,L,Oe,ge,Wt,ze,fn="GPTNeoXJapaneseTokenizer",_t,U,fe,It,Pe,_n=`This tokenizer inherits from <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> and is based on Japanese special Sub-Word-Encoding that is
used in this repository (<a href="https://github.com/tanreinama/Japanese-BPEEncoder_V2" rel="nofollow">https://github.com/tanreinama/Japanese-BPEEncoder_V2</a>). Check the repository for details.
Japanese has a relatively large vocabulary and there is no separation between words. Furthermore, the language is a
combination of hiragana, katakana, and kanji, and variants such as “1” and “①” are often used. In order to cope
with these, this tokenizer has the following features`,Bt,Ve,Tn=`<li>Subword-by-subword segmentation, which is intermediate between byte strings and morphological analysis.</li> <li>BPEs are created for each Kanji, Hiragana, and Katakana character, and there are no BPEs that cross character
types, such as Kanji + Hiragana or Hiragana + Katakana.</li> <li>All-byte encoding that does not require &lt;unk&gt;.</li> <li>Independent of UTF codes such as 2-byte and 3-byte characters</li> <li>Conversion of heterographs to the same token_id</li> <li>Emoji and Emoticon are grouped into 12 types as special tags.</li>`,St,q,Rt,H,_e,Lt,Fe,bn="Converts a sequence of tokens (string) in a single string.",Tt,E,Q,De,Te,qt,Ee,yn="GPTNeoXJapaneseModel",bt,x,be,Ht,Ze,vn=`The bare GPTNeoXJapanese Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,Qt,C,ye,At,We,Jn='The <a href="/docs/transformers/v4.33.3/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseModel">GPTNeoXJapaneseModel</a> forward method, overrides the <code>__call__</code> special method.',Yt,A,Ot,Y,yt,Z,O,Ke,ve,Dt,Ie,Mn="GPTNeoXJapaneseForCausalLM",vt,j,Je,Kt,Be,wn=`GPTNeoXJapanese Model with a <code>language modeling</code> head on top for Classifier Model fine-tuning.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,en,N,Me,tn,Se,Un='The <a href="/docs/transformers/v4.33.3/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseForCausalLM">GPTNeoXJapaneseForCausalLM</a> forward method, overrides the <code>__call__</code> special method.',nn,D,on,K,Jt,et,Mt;return ae=new oe({}),se=new oe({}),ie=new oe({}),ce=new st({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> 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;人とAIが協調するためには、&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)
人とAIが協調するためには、AIと人が共存し、AIを正しく理解する必要があります。`}}),pe=new oe({}),me=new oe({}),ue=new ke({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/v4.33.3/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py#L28"}}),R=new jt({props:{anchor:"transformers.GPTNeoXJapaneseConfig.example",$$slots:{default:[Rn]},$$scope:{ctx:k}}}),ge=new oe({}),fe=new ke({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/v4.33.3/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py#L70"}}),q=new jt({props:{anchor:"transformers.GPTNeoXJapaneseTokenizer.example",$$slots:{default:[Ln]},$$scope:{ctx:k}}}),_e=new ke({props:{name:"convert_tokens_to_string",anchor:"transformers.GPTNeoXJapaneseTokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py#L177"}}),Te=new oe({}),be=new ke({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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py#L440"}}),ye=new ke({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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py#L460",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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>) — 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>) — 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>) — 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>) — 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/v4.33.3/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast"
>transformers.modeling_outputs.BaseModelOutputWithPast</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),A=new En({props:{$$slots:{default:[qn]},$$scope:{ctx:k}}}),Y=new jt({props:{anchor:"transformers.GPTNeoXJapaneseModel.forward.example",$$slots:{default:[Hn]},$$scope:{ctx:k}}}),ve=new oe({}),Je=new ke({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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py#L595"}}),Me=new ke({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/v4.33.3/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/v4.33.3/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
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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/v4.33.3/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py#L614",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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) — 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>) — 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>) — 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>) — 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>) — 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/v4.33.3/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast"
>transformers.modeling_outputs.CausalLMOutputWithPast</a> or <code>tuple(torch.FloatTensor)</code></p>
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