import{S as kn,i as vn,s as Tn,e as o,k as d,w as E,t as s,M as bn,c as r,d as n,m,a,x as y,h as i,b as p,G as t,g as c,y as x,q as A,o as G,B as j,v as Sn,L as Pn}from"../../chunks/vendor-hf-doc-builder.js";import{D as _n}from"../../chunks/Docstring-hf-doc-builder.js";import{C as wn}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Re}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as $n}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function zn(Pe){let u,M,k,g,v;return g=new wn({props:{code:`from transformers import GPTSw3Tokenizer

tokenizer = GPTSw3Tokenizer.from_pretrained("AI-Sweden/gpt-sw3-126m")
tokenizer("Svenska \xE4r kul!")["input_ids"]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> GPTSw3Tokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = GPTSw3Tokenizer.from_pretrained(<span class="hljs-string">&quot;AI-Sweden/gpt-sw3-126m&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer(<span class="hljs-string">&quot;Svenska \xE4r kul!&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
[<span class="hljs-number">1814</span>, <span class="hljs-number">377</span>, <span class="hljs-number">3617</span>, <span class="hljs-number">63504</span>]`}}),{c(){u=o("p"),M=s("Example usage:"),k=d(),E(g.$$.fragment)},l(h){u=r(h,"P",{});var T=a(u);M=i(T,"Example usage:"),T.forEach(n),k=m(h),y(g.$$.fragment,h)},m(h,T){c(h,u,T),t(u,M),c(h,k,T),x(g,h,T),v=!0},p:Pn,i(h){v||(A(g.$$.fragment,h),v=!0)},o(h){G(g.$$.fragment,h),v=!1},d(h){h&&n(u),h&&n(k),j(g,h)}}}function En(Pe){let u,M,k,g,v,h,T,le,Ue,$e,P,I,pe,F,Ve,ce,Je,ze,L,Ke,B,Qe,Xe,Ee,Z,Ye,ye,ee,Ze,xe,C,et,O,tt,nt,Ae,f,ot,W,rt,at,de,st,it,me,lt,pt,he,ct,dt,fe,mt,ht,ue,ft,ut,ge,gt,Ge,te,_t,je,H,Me,$,q,_e,R,wt,we,kt,Ie,b,ke,ne,vt,Tt,ve,oe,bt,St,Te,re,Pt,Le,z,N,be,U,$t,Se,zt,Ce,_,V,Et,J,yt,K,xt,At,Gt,Q,jt,ae,Mt,It,Lt,D,Ct,se,X,qe;return h=new Re({}),F=new Re({}),H=new wn({props:{code:`from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AI-Sweden/gpt-sw3-356m")
model = AutoModelForCausalLM.from_pretrained("AI-Sweden/gpt-sw3-356m")

input_ids = tokenizer("Tr\xE4d \xE4r fina f\xF6r att", return_tensors="pt")["input_ids"]

generated_token_ids = model.generate(inputs=input_ids, max_new_tokens=10, do_sample=True)[0]

print(tokenizer.decode(generated_token_ids))`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;AI-Sweden/gpt-sw3-356m&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;AI-Sweden/gpt-sw3-356m&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(<span class="hljs-string">&quot;Tr\xE4d \xE4r fina f\xF6r att&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>generated_token_ids = model.generate(inputs=input_ids, max_new_tokens=<span class="hljs-number">10</span>, do_sample=<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>(tokenizer.decode(generated_token_ids))
Tr\xE4d \xE4r fina f\xF6r att de \xE4r f\xE4rgstarka. Men ibland \xE4r det fint`}}),R=new Re({}),U=new Re({}),V=new _n({props:{name:"class transformers.GPTSw3Tokenizer",anchor:"transformers.GPTSw3Tokenizer",parameters:[{name:"vocab_file",val:""},{name:"do_lower_case",val:" = False"},{name:"remove_space",val:" = False"},{name:"keep_accents",val:" = False"},{name:"pad_token",val:" = None"},{name:"unk_token",val:" = None"},{name:"eos_token",val:" = None"},{name:"bos_token",val:" = None"},{name:"sp_model_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.GPTSw3Tokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.GPTSw3Tokenizer.do_lower_case",description:`<strong>do_lower_case</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to lowercase the input when tokenizing.`,name:"do_lower_case"},{anchor:"transformers.GPTSw3Tokenizer.remove_space",description:`<strong>remove_space</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).`,name:"remove_space"},{anchor:"transformers.GPTSw3Tokenizer.keep_accents",description:`<strong>keep_accents</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to keep accents when tokenizing.`,name:"keep_accents"},{anchor:"transformers.GPTSw3Tokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The beginning of sequence token that can be used for downstream task, was not seen during pretraining. If
not provided, will default to &#x2019;<s>&#x2019; or &#x2019;&lt;|endoftext|&gt;&#x2019;, depending on model size.</s>`,name:"bos_token"},{anchor:"transformers.GPTSw3Tokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The end of sequence token seen during pretraining. If not provided, will default to &#x2019;&lt;|endoftext|&gt;&#x2019;`,name:"eos_token"},{anchor:"transformers.GPTSw3Tokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>) &#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. If not provided, will default to &#x2019;<unk>&#x2018;.</unk>`,name:"unk_token"},{anchor:"transformers.GPTSw3Tokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The token used for padding, for example when batching sequences of different lengths. If not provided, will
default to &#x2019;<pad>&#x2019; or &#x2019;<unk>&#x2019; depending on model size.</unk></pad>`,name:"pad_token"},{anchor:"transformers.GPTSw3Tokenizer.sp_model_kwargs",description:`<strong>sp_model_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Will be passed to the <code>SentencePieceProcessor.__init__()</code> method. The <a href="https://github.com/google/sentencepiece/tree/master/python" rel="nofollow">Python wrapper for
SentencePiece</a> can be used, among other things,
to set:</p>
<ul>
<li>
<p><code>enable_sampling</code>: Enable subword regularization.</p>
</li>
<li>
<p><code>nbest_size</code>: Sampling parameters for unigram. Invalid for BPE-Dropout.</p>
<ul>
<li><code>nbest_size = {0,1}</code>: No sampling is performed.</li>
<li><code>nbest_size &gt; 1</code>: samples from the nbest_size results.</li>
<li><code>nbest_size &lt; 0</code>: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.</li>
</ul>
</li>
<li>
<p><code>alpha</code>: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.</p>
</li>
</ul>`,name:"sp_model_kwargs"},{anchor:"transformers.GPTSw3Tokenizer.sp_model",description:`<strong>sp_model</strong> (<code>SentencePieceProcessor</code>) &#x2014;
The <em>SentencePiece</em> processor that is used for every conversion (string, tokens and IDs).`,name:"sp_model"},{anchor:"transformers.GPTSw3Tokenizer.whitespaces",description:`<strong>whitespaces</strong> (<code>set</code>) &#x2014;
The whitespaces that are replaced in the whitespace normalization in preprocessing.`,name:"whitespaces"},{anchor:"transformers.GPTSw3Tokenizer.non_printing_characters_re",description:`<strong>non_printing_characters_re</strong> (<code>Pattern</code>) &#x2014;
The compiled regular expression to remove non-printing characters in preprocessing.`,name:"non_printing_characters_re"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py#L42"}}),D=new $n({props:{anchor:"transformers.GPTSw3Tokenizer.example",$$slots:{default:[zn]},$$scope:{ctx:Pe}}}),X=new _n({props:{name:"save_vocabulary",anchor:"transformers.GPTSw3Tokenizer.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py#L252"}}),{c(){u=o("meta"),M=d(),k=o("h1"),g=o("a"),v=o("span"),E(h.$$.fragment),T=d(),le=o("span"),Ue=s("GPT-Sw3"),$e=d(),P=o("h2"),I=o("a"),pe=o("span"),E(F.$$.fragment),Ve=d(),ce=o("span"),Je=s("Overview"),ze=d(),L=o("p"),Ke=s(`The GPT-Sw3 model was first proposed in
`),B=o("a"),Qe=s("Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish"),Xe=s(`
by Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey \xD6hman,
Fredrik Carlsson, Magnus Sahlgren.`),Ee=d(),Z=o("p"),Ye=s("Since that first paper the authors have extended their work and trained new models on their new 1.2TB corpora named The Nordic Pile."),ye=d(),ee=o("p"),Ze=s(`GPT-Sw3 is a collection of large decoder-only pretrained transformer language models that were developed by AI Sweden
in collaboration with RISE and the WASP WARA for Media and Language. GPT-Sw3 has been trained on a dataset containing
320B tokens in Swedish, Norwegian, Danish, Icelandic, English, and programming code. The model was pretrained using a
causal language modeling (CLM) objective utilizing the NeMo Megatron GPT implementation.`),xe=d(),C=o("p"),et=s("This model was contributed by "),O=o("a"),tt=s("AI Sweden"),nt=s("."),Ae=d(),f=o("p"),ot=s("The implementation uses the "),W=o("a"),rt=s("GPT2Model"),at=s(` coupled
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implementation and the corresponding GPT2 model implementation respectively.
`),fe=o("em"),mt=s("Note that sentencepiece is required to use our tokenizer and can be installed with:"),ht=d(),ue=o("code"),ft=s("pip install transformers[sentencepiece]"),ut=s(" or "),ge=o("code"),gt=s("pip install sentencepiece"),Ge=d(),te=o("p"),_t=s("Example usage:"),je=d(),E(H.$$.fragment),Me=d(),$=o("h2"),q=o("a"),_e=o("span"),E(R.$$.fragment),wt=d(),we=o("span"),kt=s("Documentation resources"),Ie=d(),b=o("ul"),ke=o("li"),ne=o("a"),vt=s("Text classification task guide"),Tt=d(),ve=o("li"),oe=o("a"),bt=s("Token classification task guide"),St=d(),Te=o("li"),re=o("a"),Pt=s("Causal language modeling task guide"),Le=d(),z=o("h2"),N=o("a"),be=o("span"),E(U.$$.fragment),$t=d(),Se=o("span"),zt=s("GPTSw3Tokenizer"),Ce=d(),_=o("div"),E(V.$$.fragment),Et=d(),J=o("p"),yt=s("Construct an GPTSw3 tokenizer. Based on "),K=o("a"),xt=s("SentencePiece"),At=s("."),Gt=d(),Q=o("p"),jt=s("This tokenizer inherits from "),ae=o("a"),Mt=s("PreTrainedTokenizer"),It=s(` which contains most of the main methods. Users should refer to
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