import{S as Kp,i as Zp,s as ed,e as s,k as d,w as m,t as r,M as td,c as o,d as a,m as f,a as l,x as _,h as n,b as u,G as t,g as p,y as g,q as w,o as v,B as y,v as ad}from"../chunks/vendor-hf-doc-builder.js";import{T as Wo}from"../chunks/Tip-hf-doc-builder.js";import{I}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as j}from"../chunks/CodeBlock-hf-doc-builder.js";function sd(O){let h,E,c,$,S;return{c(){h=s("p"),E=r(`The dataset script is optional if your dataset is in one of the following formats: CSV, JSON, JSON lines, text or Parquet.
With those formats, you should be able to load your dataset automatically with `),c=s("a"),$=r("load_dataset()"),S=r("."),this.h()},l(q){h=o(q,"P",{});var b=l(h);E=n(b,`The dataset script is optional if your dataset is in one of the following formats: CSV, JSON, JSON lines, text or Parquet.
With those formats, you should be able to load your dataset automatically with `),c=o(b,"A",{href:!0});var x=l(c);$=n(x,"load_dataset()"),x.forEach(a),S=n(b,"."),b.forEach(a),this.h()},h(){u(c,"href","/docs/datasets/v2.11.0/en/package_reference/loading_methods#datasets.load_dataset")},m(q,b){p(q,h,b),t(h,E),t(h,c),t(c,$),t(h,S)},d(q){q&&a(h)}}}function od(O){let h,E,c,$,S;return{c(){h=s("p"),E=r("To help you get started, try beginning with the dataset loading script "),c=s("a"),$=r("template"),S=r("!"),this.h()},l(q){h=o(q,"P",{});var b=l(h);E=n(b,"To help you get started, try beginning with the dataset loading script "),c=o(b,"A",{href:!0,rel:!0});var x=l(c);$=n(x,"template"),x.forEach(a),S=n(b,"!"),b.forEach(a),this.h()},h(){u(c,"href","https://github.com/huggingface/datasets/blob/main/templates/new_dataset_script.py"),u(c,"rel","nofollow")},m(q,b){p(q,h,b),t(h,E),t(h,c),t(c,$),t(h,S)},d(q){q&&a(h)}}}function ld(O){let h,E;return{c(){h=s("p"),E=r("Only use a default configuration when it makes sense. Don\u2019t set one because it may be more convenient for the user to not specify a configuration when they load your dataset. For example, multi-lingual datasets often have a separate configuration for each language. An appropriate default may be an aggregated configuration that loads all the languages of the dataset if the user doesn\u2019t request a particular one.")},l(c){h=o(c,"P",{});var $=l(h);E=n($,"Only use a default configuration when it makes sense. Don\u2019t set one because it may be more convenient for the user to not specify a configuration when they load your dataset. For example, multi-lingual datasets often have a separate configuration for each language. An appropriate default may be an aggregated configuration that loads all the languages of the dataset if the user doesn\u2019t request a particular one."),$.forEach(a)},m(c,$){p(c,h,$),t(h,E)},d(c){c&&a(h)}}}function rd(O){let h,E;return{c(){h=s("p"),E=r("If the data files live in the same folder or repository of the dataset script, you can just pass the relative paths to the files instead of URLs.")},l(c){h=o(c,"P",{});var $=l(h);E=n($,"If the data files live in the same folder or repository of the dataset script, you can just pass the relative paths to the files instead of URLs."),$.forEach(a)},m(c,$){p(c,h,$),t(h,E)},d(c){c&&a(h)}}}function nd(O){let h,E,c,$,S,q,b,x,Ko,hs,ae,cs,ht,Zo,ms,ct,el,_s,T,tl,Qt,al,sl,Yt,ol,ll,gs,Ae,ws,Se,vs,mt,rl,ys,k,Xt,nl,il,Jt,pl,dl,Wt,fl,ul,Kt,hl,cl,Zt,ml,$s,se,_l,De,gl,wl,Es,oe,qs,H,le,ea,xe,vl,ta,yl,bs,re,$l,aa,El,ql,js,ne,sa,_t,oa,bl,jl,kl,la,ie,ra,Al,Sl,gt,Dl,xl,ks,Ie,As,z,na,wt,ia,Il,Ol,Tl,pa,vt,da,Cl,Ll,Ss,yt,Nl,Ds,Oe,xs,Q,pe,fa,Te,Pl,ua,Bl,Is,C,Gl,Ce,Rl,Ul,$t,Vl,Fl,Os,de,Ml,Le,Hl,zl,Ts,Et,Ne,Ql,qt,Yl,Xl,Cs,Pe,Ls,Be,Ge,Jl,ha,Wl,Kl,Ns,Re,Ps,Ue,Ve,Zl,ca,er,tr,Bs,Fe,Gs,Y,fe,ma,Me,ar,_a,sr,Rs,L,or,ga,lr,rr,wa,nr,ir,Us,He,Vs,ue,Fs,X,he,va,ze,pr,ya,dr,Ms,bt,fr,Hs,jt,$a,ur,zs,Qe,Qs,ce,Ys,Ye,Xe,me,kt,hr,cr,At,mr,_r,gr,Je,Ea,D,wr,qa,vr,yr,ba,$r,Er,ja,qr,br,ka,jr,kr,Ar,Aa,St,Sa,Sr,Dr,Xs,_e,xr,Da,Ir,Or,Js,We,Ws,J,ge,xa,Ke,Tr,Ia,Cr,Ks,Dt,Lr,Zs,N,Oa,Nr,Pr,Ta,Br,Gr,Ca,Rr,eo,xt,Ur,to,we,La,ve,Na,Vr,Fr,Pa,Mr,Hr,zr,Ba,Ze,Qr,Ga,Yr,Xr,ao,et,so,W,ye,Ra,tt,Jr,Ua,Wr,oo,P,Kr,Va,Zr,en,Fa,tn,an,lo,$e,sn,Ma,on,ln,ro,at,no,B,rn,Ha,nn,pn,za,dn,fn,io,K,Ee,Qa,st,un,Ya,hn,po,G,cn,It,mn,_n,Ot,gn,wn,fo,Tt,vn,uo,ot,ho,Z,qe,Xa,lt,yn,Ja,$n,co,ee,be,Wa,rt,En,Ka,qn,mo,je,bn,nt,jn,kn,_o,R,An,Za,Sn,Dn,es,xn,In,go,it,wo,U,On,ts,Tn,Cn,as,Ln,Nn,vo,te,ke,ss,pt,Pn,os,Bn,yo,Ct,Gn,$o,dt,Eo,A,Rn,Lt,Un,Vn,Nt,Fn,Mn,ls,Hn,zn,rs,Qn,Yn,qo,ft,bo,Pt,Xn,jo;return q=new I({}),ae=new Wo({props:{$$slots:{default:[sd]},$$scope:{ctx:O}}}),Ae=new j({props:{code:`my_dataset/
\u251C\u2500\u2500 README.md
\u2514\u2500\u2500 my_dataset.py`,highlighted:`my<span class="hljs-emphasis">_dataset/
\u251C\u2500\u2500 README.md
\u2514\u2500\u2500 my_</span>dataset.py`}}),Se=new j({props:{code:`from datasets import load_dataset
load_dataset("path/to/my_dataset")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span>load_dataset(<span class="hljs-string">&quot;path/to/my_dataset&quot;</span>)`}}),oe=new Wo({props:{$$slots:{default:[od]},$$scope:{ctx:O}}}),xe=new I({}),Ie=new j({props:{code:`datasets.Features(
    {
        "id": datasets.Value("string"),
        "title": datasets.Value("string"),
        "context": datasets.Value("string"),
        "question": datasets.Value("string"),
        "answers": datasets.Sequence(
            {
                "text": datasets.Value("string"),
                "answer_start": datasets.Value("int32"),
            }
        ),
    }
)`,highlighted:`datasets.Features(
    {
        <span class="hljs-string">&quot;id&quot;</span>: datasets.Value(<span class="hljs-string">&quot;string&quot;</span>),
        <span class="hljs-string">&quot;title&quot;</span>: datasets.Value(<span class="hljs-string">&quot;string&quot;</span>),
        <span class="hljs-string">&quot;context&quot;</span>: datasets.Value(<span class="hljs-string">&quot;string&quot;</span>),
        <span class="hljs-string">&quot;question&quot;</span>: datasets.Value(<span class="hljs-string">&quot;string&quot;</span>),
        <span class="hljs-string">&quot;answers&quot;</span>: datasets.<span class="hljs-type">Sequence</span>(
            {
                <span class="hljs-string">&quot;text&quot;</span>: datasets.Value(<span class="hljs-string">&quot;string&quot;</span>),
                <span class="hljs-string">&quot;answer_start&quot;</span>: datasets.Value(<span class="hljs-string">&quot;int32&quot;</span>),
            }
        ),
    }
)`}}),Oe=new j({props:{code:`def _info(self):
    return datasets.DatasetInfo(
        description=_DESCRIPTION,
        features=datasets.Features(
            {
                "id": datasets.Value("string"),
                "title": datasets.Value("string"),
                "context": datasets.Value("string"),
                "question": datasets.Value("string"),
                "answers": datasets.features.Sequence(
                    {"text": datasets.Value("string"), "answer_start": datasets.Value("int32"),}
                ),
            }
        ),
        # No default supervised_keys (as we have to pass both question
        # and context as input).
        supervised_keys=None,
        homepage="https://rajpurkar.github.io/SQuAD-explorer/",
        citation=_CITATION,
    )`,highlighted:`<span class="hljs-keyword">def</span> <span class="hljs-title function_">_info</span>(<span class="hljs-params">self</span>):
    <span class="hljs-keyword">return</span> datasets.DatasetInfo(
        description=_DESCRIPTION,
        features=datasets.Features(
            {
                <span class="hljs-string">&quot;id&quot;</span>: datasets.Value(<span class="hljs-string">&quot;string&quot;</span>),
                <span class="hljs-string">&quot;title&quot;</span>: datasets.Value(<span class="hljs-string">&quot;string&quot;</span>),
                <span class="hljs-string">&quot;context&quot;</span>: datasets.Value(<span class="hljs-string">&quot;string&quot;</span>),
                <span class="hljs-string">&quot;question&quot;</span>: datasets.Value(<span class="hljs-string">&quot;string&quot;</span>),
                <span class="hljs-string">&quot;answers&quot;</span>: datasets.features.<span class="hljs-type">Sequence</span>(
                    {<span class="hljs-string">&quot;text&quot;</span>: datasets.Value(<span class="hljs-string">&quot;string&quot;</span>), <span class="hljs-string">&quot;answer_start&quot;</span>: datasets.Value(<span class="hljs-string">&quot;int32&quot;</span>),}
                ),
            }
        ),
        <span class="hljs-comment"># No default supervised_keys (as we have to pass both question</span>
        <span class="hljs-comment"># and context as input).</span>
        supervised_keys=<span class="hljs-literal">None</span>,
        homepage=<span class="hljs-string">&quot;https://rajpurkar.github.io/SQuAD-explorer/&quot;</span>,
        citation=_CITATION,
    )`}}),Te=new I({}),Pe=new j({props:{code:`class SuperGlueConfig(datasets.BuilderConfig):
    """BuilderConfig for SuperGLUE."""

    def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
        """BuilderConfig for SuperGLUE.

        Args:
        features: *list[string]*, list of the features that will appear in the
            feature dict. Should not include "label".
        data_url: *string*, url to download the zip file from.
        citation: *string*, citation for the data set.
        url: *string*, url for information about the data set.
        label_classes: *list[string]*, the list of classes for the label if the
            label is present as a string. Non-string labels will be cast to either
            'False' or 'True'.
        **kwargs: keyword arguments forwarded to super.
        """
        # Version history:
        # 1.0.2: Fixed non-nondeterminism in ReCoRD.
        # 1.0.1: Change from the pre-release trial version of SuperGLUE (v1.9) to
        #        the full release (v2.0).
        # 1.0.0: S3 (new shuffling, sharding and slicing mechanism).
        # 0.0.2: Initial version.
        super().__init__(version=datasets.Version("1.0.2"), **kwargs)
        self.features = features
        self.label_classes = label_classes
        self.data_url = data_url
        self.citation = citation
        self.url = url`,highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">SuperGlueConfig</span>(datasets.BuilderConfig):
    <span class="hljs-string">&quot;&quot;&quot;BuilderConfig for SuperGLUE.&quot;&quot;&quot;</span>

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, features, data_url, citation, url, label_classes=(<span class="hljs-params"><span class="hljs-string">&quot;False&quot;</span>, <span class="hljs-string">&quot;True&quot;</span></span>), **kwargs</span>):
        <span class="hljs-string">&quot;&quot;&quot;BuilderConfig for SuperGLUE.

        Args:
        features: *list[string]*, list of the features that will appear in the
            feature dict. Should not include &quot;label&quot;.
        data_url: *string*, url to download the zip file from.
        citation: *string*, citation for the data set.
        url: *string*, url for information about the data set.
        label_classes: *list[string]*, the list of classes for the label if the
            label is present as a string. Non-string labels will be cast to either
            &#x27;False&#x27; or &#x27;True&#x27;.
        **kwargs: keyword arguments forwarded to super.
        &quot;&quot;&quot;</span>
        <span class="hljs-comment"># Version history:</span>
        <span class="hljs-comment"># 1.0.2: Fixed non-nondeterminism in ReCoRD.</span>
        <span class="hljs-comment"># 1.0.1: Change from the pre-release trial version of SuperGLUE (v1.9) to</span>
        <span class="hljs-comment">#        the full release (v2.0).</span>
        <span class="hljs-comment"># 1.0.0: S3 (new shuffling, sharding and slicing mechanism).</span>
        <span class="hljs-comment"># 0.0.2: Initial version.</span>
        <span class="hljs-built_in">super</span>().__init__(version=datasets.Version(<span class="hljs-string">&quot;1.0.2&quot;</span>), **kwargs)
        self.features = features
        self.label_classes = label_classes
        self.data_url = data_url
        self.citation = citation
        self.url = url`}}),Re=new j({props:{code:`class SuperGlue(datasets.GeneratorBasedBuilder):
    """The SuperGLUE benchmark."""

    BUILDER_CONFIGS = [
        SuperGlueConfig(
            name="boolq",
            description=_BOOLQ_DESCRIPTION,
            features=["question", "passage"],
            data_url="https://dl.fbaipublicfiles.com/glue/superglue/data/v2/BoolQ.zip",
            citation=_BOOLQ_CITATION,
            url="https://github.com/google-research-datasets/boolean-questions",
        ),
        ...
        ...
        SuperGlueConfig(
            name="axg",
            description=_AXG_DESCRIPTION,
            features=["premise", "hypothesis"],
            label_classes=["entailment", "not_entailment"],
            data_url="https://dl.fbaipublicfiles.com/glue/superglue/data/v2/AX-g.zip",
            citation=_AXG_CITATION,
            url="https://github.com/rudinger/winogender-schemas",
        ),`,highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">SuperGlue</span>(datasets.GeneratorBasedBuilder):
    <span class="hljs-string">&quot;&quot;&quot;The SuperGLUE benchmark.&quot;&quot;&quot;</span>

    BUILDER_CONFIGS = [
        SuperGlueConfig(
            name=<span class="hljs-string">&quot;boolq&quot;</span>,
            description=_BOOLQ_DESCRIPTION,
            features=[<span class="hljs-string">&quot;question&quot;</span>, <span class="hljs-string">&quot;passage&quot;</span>],
            data_url=<span class="hljs-string">&quot;https://dl.fbaipublicfiles.com/glue/superglue/data/v2/BoolQ.zip&quot;</span>,
            citation=_BOOLQ_CITATION,
            url=<span class="hljs-string">&quot;https://github.com/google-research-datasets/boolean-questions&quot;</span>,
        ),
        ...
        ...
        SuperGlueConfig(
            name=<span class="hljs-string">&quot;axg&quot;</span>,
            description=_AXG_DESCRIPTION,
            features=[<span class="hljs-string">&quot;premise&quot;</span>, <span class="hljs-string">&quot;hypothesis&quot;</span>],
            label_classes=[<span class="hljs-string">&quot;entailment&quot;</span>, <span class="hljs-string">&quot;not_entailment&quot;</span>],
            data_url=<span class="hljs-string">&quot;https://dl.fbaipublicfiles.com/glue/superglue/data/v2/AX-g.zip&quot;</span>,
            citation=_AXG_CITATION,
            url=<span class="hljs-string">&quot;https://github.com/rudinger/winogender-schemas&quot;</span>,
        ),`}}),Fe=new j({props:{code:`from datasets import load_dataset
dataset = load_dataset('super_glue', 'boolq')`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&#x27;super_glue&#x27;</span>, <span class="hljs-string">&#x27;boolq&#x27;</span>)`}}),Me=new I({}),He=new j({props:{code:`class NewDataset(datasets.GeneratorBasedBuilder):

VERSION = datasets.Version("1.1.0")

BUILDER_CONFIGS = [
    datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
    datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
]

DEFAULT_CONFIG_NAME = "first_domain"`,highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">NewDataset</span>(datasets.GeneratorBasedBuilder):

VERSION = datasets.Version(<span class="hljs-string">&quot;1.1.0&quot;</span>)

BUILDER_CONFIGS = [
    datasets.BuilderConfig(name=<span class="hljs-string">&quot;first_domain&quot;</span>, version=VERSION, description=<span class="hljs-string">&quot;This part of my dataset covers a first domain&quot;</span>),
    datasets.BuilderConfig(name=<span class="hljs-string">&quot;second_domain&quot;</span>, version=VERSION, description=<span class="hljs-string">&quot;This part of my dataset covers a second domain&quot;</span>),
]

DEFAULT_CONFIG_NAME = <span class="hljs-string">&quot;first_domain&quot;</span>`}}),ue=new Wo({props:{warning:!0,$$slots:{default:[ld]},$$scope:{ctx:O}}}),ze=new I({}),Qe=new j({props:{code:`_URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
_URLS = {
    "train": _URL + "train-v1.1.json",
    "dev": _URL + "dev-v1.1.json",
}`,highlighted:`_URL = <span class="hljs-string">&quot;https://rajpurkar.github.io/SQuAD-explorer/dataset/&quot;</span>
_URLS = {
    <span class="hljs-string">&quot;train&quot;</span>: _URL + <span class="hljs-string">&quot;train-v1.1.json&quot;</span>,
    <span class="hljs-string">&quot;dev&quot;</span>: _URL + <span class="hljs-string">&quot;dev-v1.1.json&quot;</span>,
}`}}),ce=new Wo({props:{$$slots:{default:[rd]},$$scope:{ctx:O}}}),We=new j({props:{code:`def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
    urls_to_download = self._URLS
    downloaded_files = dl_manager.download_and_extract(urls_to_download)

    return [
        datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
        datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
    ]`,highlighted:`<span class="hljs-keyword">def</span> <span class="hljs-title function_">_split_generators</span>(<span class="hljs-params">self, dl_manager: datasets.DownloadManager</span>) -&gt; <span class="hljs-type">List</span>[datasets.SplitGenerator]:
    urls_to_download = self._URLS
    downloaded_files = dl_manager.download_and_extract(urls_to_download)

    <span class="hljs-keyword">return</span> [
        datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={<span class="hljs-string">&quot;filepath&quot;</span>: downloaded_files[<span class="hljs-string">&quot;train&quot;</span>]}),
        datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={<span class="hljs-string">&quot;filepath&quot;</span>: downloaded_files[<span class="hljs-string">&quot;dev&quot;</span>]}),
    ]`}}),Ke=new I({}),et=new j({props:{code:`def _generate_examples(self, filepath):
    """This function returns the examples in the raw (text) form."""
    logger.info("generating examples from = %s", filepath)
    with open(filepath) as f:
        squad = json.load(f)
        for article in squad["data"]:
            title = article.get("title", "").strip()
            for paragraph in article["paragraphs"]:
                context = paragraph["context"].strip()
                for qa in paragraph["qas"]:
                    question = qa["question"].strip()
                    id_ = qa["id"]

                    answer_starts = [answer["answer_start"] for answer in qa["answers"]]
                    answers = [answer["text"].strip() for answer in qa["answers"]]

                    # Features currently used are "context", "question", and "answers".
                    # Others are extracted here for the ease of future expansions.
                    yield id_, {
                        "title": title,
                        "context": context,
                        "question": question,
                        "id": id_,
                        "answers": {"answer_start": answer_starts, "text": answers,},
                    }`,highlighted:`<span class="hljs-keyword">def</span> <span class="hljs-title function_">_generate_examples</span>(<span class="hljs-params">self, filepath</span>):
    <span class="hljs-string">&quot;&quot;&quot;This function returns the examples in the raw (text) form.&quot;&quot;&quot;</span>
    logger.info(<span class="hljs-string">&quot;generating examples from = %s&quot;</span>, filepath)
    <span class="hljs-keyword">with</span> <span class="hljs-built_in">open</span>(filepath) <span class="hljs-keyword">as</span> f:
        squad = json.load(f)
        <span class="hljs-keyword">for</span> article <span class="hljs-keyword">in</span> squad[<span class="hljs-string">&quot;data&quot;</span>]:
            title = article.get(<span class="hljs-string">&quot;title&quot;</span>, <span class="hljs-string">&quot;&quot;</span>).strip()
            <span class="hljs-keyword">for</span> paragraph <span class="hljs-keyword">in</span> article[<span class="hljs-string">&quot;paragraphs&quot;</span>]:
                context = paragraph[<span class="hljs-string">&quot;context&quot;</span>].strip()
                <span class="hljs-keyword">for</span> qa <span class="hljs-keyword">in</span> paragraph[<span class="hljs-string">&quot;qas&quot;</span>]:
                    question = qa[<span class="hljs-string">&quot;question&quot;</span>].strip()
                    id_ = qa[<span class="hljs-string">&quot;id&quot;</span>]

                    answer_starts = [answer[<span class="hljs-string">&quot;answer_start&quot;</span>] <span class="hljs-keyword">for</span> answer <span class="hljs-keyword">in</span> qa[<span class="hljs-string">&quot;answers&quot;</span>]]
                    answers = [answer[<span class="hljs-string">&quot;text&quot;</span>].strip() <span class="hljs-keyword">for</span> answer <span class="hljs-keyword">in</span> qa[<span class="hljs-string">&quot;answers&quot;</span>]]

                    <span class="hljs-comment"># Features currently used are &quot;context&quot;, &quot;question&quot;, and &quot;answers&quot;.</span>
                    <span class="hljs-comment"># Others are extracted here for the ease of future expansions.</span>
                    <span class="hljs-keyword">yield</span> id_, {
                        <span class="hljs-string">&quot;title&quot;</span>: title,
                        <span class="hljs-string">&quot;context&quot;</span>: context,
                        <span class="hljs-string">&quot;question&quot;</span>: question,
                        <span class="hljs-string">&quot;id&quot;</span>: id_,
                        <span class="hljs-string">&quot;answers&quot;</span>: {<span class="hljs-string">&quot;answer_start&quot;</span>: answer_starts, <span class="hljs-string">&quot;text&quot;</span>: answers,},
                    }`}}),tt=new I({}),at=new j({props:{code:"datasets-cli test path/to/<your-dataset-loading-script> --save_info --all_configs",highlighted:'datasets-cli test path/<span class="hljs-keyword">to</span>/&lt;your-dataset-loading-<span class="hljs-keyword">script</span>&gt; <span class="hljs-comment">--save_info --all_configs</span>'}}),st=new I({}),ot=new j({props:{code:`from datasets import load_dataset
load_dataset("<username>/my_dataset")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span>load_dataset(<span class="hljs-string">&quot;&lt;username&gt;/my_dataset&quot;</span>)`}}),lt=new I({}),rt=new I({}),it=new j({props:{code:`
class MyShardedDataset(datasets.GeneratorBasedBuilder):

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        downloaded_files = dl_manager.download([f"data/shard_{i}.jsonl" for i in range(1024)])
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files}),
        ]

    def _generate_examples(self, filepaths):
        # Each worker can be given a slice of the original \`filepaths\` list defined in the \`gen_kwargs\`
        # so that this code can run in parallel on several shards at the same time
        for filepath in filepaths:
            ...`,highlighted:`
<span class="hljs-keyword">class</span> <span class="hljs-title class_">MyShardedDataset</span>(datasets.GeneratorBasedBuilder):

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">_split_generators</span>(<span class="hljs-params">self, dl_manager: datasets.DownloadManager</span>) -&gt; <span class="hljs-type">List</span>[datasets.SplitGenerator]:
        downloaded_files = dl_manager.download([<span class="hljs-string">f&quot;data/shard_<span class="hljs-subst">{i}</span>.jsonl&quot;</span> <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">1024</span>)])
        <span class="hljs-keyword">return</span> [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={<span class="hljs-string">&quot;filepaths&quot;</span>: downloaded_files}),
        ]

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">_generate_examples</span>(<span class="hljs-params">self, filepaths</span>):
        <span class="hljs-comment"># Each worker can be given a slice of the original \`filepaths\` list defined in the \`gen_kwargs\`</span>
        <span class="hljs-comment"># so that this code can run in parallel on several shards at the same time</span>
        <span class="hljs-keyword">for</span> filepath <span class="hljs-keyword">in</span> filepaths:
            ...`}}),pt=new I({}),dt=new j({props:{code:`import pyarrow as pa
pa_table = pa.Table.from_pandas(df)`,highlighted:`<span class="hljs-keyword">import</span> pyarrow <span class="hljs-keyword">as</span> pa
pa_table = pa.Table.from_pandas(df)`}}),ft=new j({props:{code:`class MySuperFastDataset(datasets.ArrowBasedBuilder):

    def _generate_tables(self, filepaths):
        idx = 0
        for filepath in filepaths:
            ...
            yield idx, pa_table
            idx += 1`,highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">MySuperFastDataset</span>(datasets.ArrowBasedBuilder):

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">_generate_tables</span>(<span class="hljs-params">self, filepaths</span>):
        idx = <span class="hljs-number">0</span>
        <span class="hljs-keyword">for</span> filepath <span class="hljs-keyword">in</span> filepaths:
            ...
            <span class="hljs-keyword">yield</span> idx, pa_table
            idx += <span class="hljs-number">1</span>`}}),{c(){h=s("meta"),E=d(),c=s("h1"),$=s("a"),S=s("span"),m(q.$$.fragment),b=d(),x=s("span"),Ko=r("Create a dataset loading script"),hs=d(),m(ae.$$.fragment),cs=d(),ht=s("p"),Zo=r("Write a dataset script to load and share your own datasets. It is a Python file that defines the different configurations and splits of your dataset, as well as how to download and process the data."),ms=d(),ct=s("p"),el=r("The script can download data files from any website, or from the same dataset repository."),_s=d(),T=s("p"),tl=r("A dataset loading script should have the same name as a dataset repository or directory. For example, a repository named "),Qt=s("code"),al=r("my_dataset"),sl=r(" should contain "),Yt=s("code"),ol=r("my_dataset.py"),ll=r(" script. 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It can help if you have hundreds or thousands of TAR archives, or JSONL files like `),nt=s("a"),jn=r("oscar"),kn=r(" for example."),_o=d(),R=s("p"),An=r("To make it work, we consider lists of files in "),Za=s("code"),Sn=r("gen_kwargs"),Dn=r(` to be shards.
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You can speed up the dataset generation by yielding Arrow tables directly, instead of examples.
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It includes information like the number of examples required to confirm the dataset was correctly generated, and information about the dataset like its "),Fa=o(Vt,"CODE",{});var Ep=l(Fa);tn=n(Ep,"features"),Ep.forEach(a),an=n(Vt,"."),Vt.forEach(a),lo=f(e),$e=o(e,"P",{});var Ho=l($e);sn=n(Ho,"Run the following command to generate your dataset metadata in "),Ma=o(Ho,"CODE",{});var qp=l(Ma);on=n(qp,"README.md"),qp.forEach(a),ln=n(Ho," and make sure your new dataset loading script works correctly:"),Ho.forEach(a),ro=f(e),_(at.$$.fragment,e),no=f(e),B=o(e,"P",{});var Ft=l(B);rn=n(Ft,"If your dataset loading script passed the test, you should now have a "),Ha=o(Ft,"CODE",{});var bp=l(Ha);nn=n(bp,"README.md"),bp.forEach(a),pn=n(Ft," file in your dataset folder containing a "),za=o(Ft,"CODE",{});var jp=l(za);dn=n(jp,"dataset_info"),jp.forEach(a),fn=n(Ft," field with some metadata."),Ft.forEach(a),io=f(e),K=o(e,"H2",{class:!0});var zo=l(K);Ee=o(zo,"A",{id:!0,class:!0,href:!0});var kp=l(Ee);Qa=o(kp,"SPAN",{});var Ap=l(Qa);_(st.$$.fragment,Ap),Ap.forEach(a),kp.forEach(a),un=f(zo),Ya=o(zo,"SPAN",{});var Sp=l(Ya);hn=n(Sp,"Upload to the Hub"),Sp.forEach(a),zo.forEach(a),po=f(e),G=o(e,"P",{});var Mt=l(G);cn=n(Mt,"Once your script is ready, "),It=o(Mt,"A",{href:!0});var Dp=l(It);mn=n(Dp,"create a dataset card"),Dp.forEach(a),_n=n(Mt," and "),Ot=o(Mt,"A",{href:!0});var xp=l(Ot);gn=n(xp,"upload it to the Hub"),xp.forEach(a),wn=n(Mt,"."),Mt.forEach(a),fo=f(e),Tt=o(e,"P",{});var Ip=l(Tt);vn=n(Ip,"Congratulations, you can now load your dataset from the Hub! \u{1F973}"),Ip.forEach(a),uo=f(e),_(ot.$$.fragment,e),ho=f(e),Z=o(e,"H2",{class:!0});var Qo=l(Z);qe=o(Qo,"A",{id:!0,class:!0,href:!0});var Op=l(qe);Xa=o(Op,"SPAN",{});var Tp=l(Xa);_(lt.$$.fragment,Tp),Tp.forEach(a),Op.forEach(a),yn=f(Qo),Ja=o(Qo,"SPAN",{});var Cp=l(Ja);$n=n(Cp,"Advanced features"),Cp.forEach(a),Qo.forEach(a),co=f(e),ee=o(e,"H3",{class:!0});var Yo=l(ee);be=o(Yo,"A",{id:!0,class:!0,href:!0});var Lp=l(be);Wa=o(Lp,"SPAN",{});var Np=l(Wa);_(rt.$$.fragment,Np),Np.forEach(a),Lp.forEach(a),En=f(Yo),Ka=o(Yo,"SPAN",{});var Pp=l(Ka);qn=n(Pp,"Sharding"),Pp.forEach(a),Yo.forEach(a),mo=f(e),je=o(e,"P",{});var Xo=l(je);bn=n(Xo,`If your dataset is made of many big files, \u{1F917} Datasets automatically runs your script in parallel to make it super fast!
It can help if you have hundreds or thousands of TAR archives, or JSONL files like `),nt=o(Xo,"A",{href:!0,rel:!0});var Bp=l(nt);jn=n(Bp,"oscar"),Bp.forEach(a),kn=n(Xo," for example."),Xo.forEach(a),_o=f(e),R=o(e,"P",{});var Ht=l(R);An=n(Ht,"To make it work, we consider lists of files in "),Za=o(Ht,"CODE",{});var Gp=l(Za);Sn=n(Gp,"gen_kwargs"),Gp.forEach(a),Dn=n(Ht,` to be shards.
Therefore \u{1F917} Datasets can automatically spawn several workers to run `),es=o(Ht,"CODE",{});var Rp=l(es);xn=n(Rp,"_generate_examples"),Rp.forEach(a),In=n(Ht," in parallel, and each worker is given a subset of shards to process."),Ht.forEach(a),go=f(e),_(it.$$.fragment,e),wo=f(e),U=o(e,"P",{});var zt=l(U);On=n(zt,"Users can also specify "),ts=o(zt,"CODE",{});var Up=l(ts);Tn=n(Up,"num_proc="),Up.forEach(a),Cn=n(zt," in "),as=o(zt,"CODE",{});var Vp=l(as);Ln=n(Vp,"load_dataset()"),Vp.forEach(a),Nn=n(zt," to specify the number of processes to use as workers."),zt.forEach(a),vo=f(e),te=o(e,"H3",{class:!0});var Jo=l(te);ke=o(Jo,"A",{id:!0,class:!0,href:!0});var Fp=l(ke);ss=o(Fp,"SPAN",{});var Mp=l(ss);_(pt.$$.fragment,Mp),Mp.forEach(a),Fp.forEach(a),Pn=f(Jo),os=o(Jo,"SPAN",{});var Hp=l(os);Bn=n(Hp,"ArrowBasedBuilder"),Hp.forEach(a),Jo.forEach(a),yo=f(e),Ct=o(e,"P",{});var zp=l(Ct);Gn=n(zp,`For some datasets it can be much faster to yield batches of data rather than examples one by one.
You can speed up the dataset generation by yielding Arrow tables directly, instead of examples.
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id={local:"create-a-dataset-loading-script",sections:[{local:"add-dataset-attributes",sections:[{local:"multiple-configurations",title:"Multiple configurations"},{local:"default-configurations",title:"Default configurations"}],title:"Add dataset attributes"},{local:"download-data-files-and-organize-splits",title:"Download data files and organize splits"},{local:"generate-samples",title:"Generate samples"},{local:"optional-generate-dataset-metadata",title:"(Optional) Generate dataset metadata"},{local:"upload-to-the-hub",title:"Upload to the Hub"},{local:"advanced-features",sections:[{local:"sharding",title:"Sharding"},{local:"arrowbasedbuilder",title:"ArrowBasedBuilder"}],title:"Advanced features"}],title:"Create a dataset loading script"};function pd(O){return ad(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class cd extends Kp{constructor(h){super();Zp(this,h,pd,nd,ed,{})}}export{cd as default,id as metadata};
