import{S as ng,i as dg,s as lg,e as o,k as m,w as b,t as c,M as ig,c as r,d as a,m as g,a as d,x as v,h as p,b as y,G as e,g as $,y as w,q as x,o as D,B as E,v as cg,L as S}from"../../chunks/vendor-hf-doc-builder.js";import{D as j}from"../../chunks/Docstring-hf-doc-builder.js";import{C as I}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as wr}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as T}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function pg(k){let l,_,f,n,u;return n=new I({props:{code:`from datasets import load_dataset_builder
builder = load_dataset_builder('rotten_tomatoes')
ds = builder.download_and_prepare()
ds = builder.as_dataset(split='train')
ds`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset_builder
<span class="hljs-meta">&gt;&gt;&gt; </span>builder = load_dataset_builder(<span class="hljs-string">&#x27;rotten_tomatoes&#x27;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = builder.download_and_prepare()
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = builder.as_dataset(split=<span class="hljs-string">&#x27;train&#x27;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds
Dataset({
    features: [<span class="hljs-string">&#x27;text&#x27;</span>, <span class="hljs-string">&#x27;label&#x27;</span>],
    num_rows: <span class="hljs-number">8530</span>
})`}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function mg(k){let l,_,f,n,u,t,i,de;return i=new I({props:{code:`from datasets import load_dataset_builder
builder = load_dataset_builder("rotten_tomatoes")
ds = builder.download_and_prepare()`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset_builder
<span class="hljs-meta">&gt;&gt;&gt; </span>builder = load_dataset_builder(<span class="hljs-string">&quot;rotten_tomatoes&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = builder.download_and_prepare()`}}),{c(){l=o("p"),_=c("Download and prepare the dataset as Arrow files that can be loaded as a Dataset using "),f=o("code"),n=c("builder.as_dataset()"),u=c(":"),t=m(),b(i.$$.fragment)},l(R){l=r(R,"P",{});var H=d(l);_=p(H,"Download and prepare the dataset as Arrow files that can be loaded as a Dataset using "),f=r(H,"CODE",{});var K=d(f);n=p(K,"builder.as_dataset()"),K.forEach(a),u=p(H,":"),H.forEach(a),t=g(R),v(i.$$.fragment,R)},m(R,H){$(R,l,H),e(l,_),e(l,f),e(f,n),e(l,u),$(R,t,H),w(i,R,H),de=!0},p:S,i(R){de||(x(i.$$.fragment,R),de=!0)},o(R){D(i.$$.fragment,R),de=!1},d(R){R&&a(l),R&&a(t),E(i,R)}}}function gg(k){let l,_,f,n,u;return n=new I({props:{code:`from datasets import load_dataset_builder
builder = load_dataset_builder("rotten_tomatoes")
ds = builder.download_and_prepare("./output_dir", file_format="parquet")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset_builder
<span class="hljs-meta">&gt;&gt;&gt; </span>builder = load_dataset_builder(<span class="hljs-string">&quot;rotten_tomatoes&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = builder.download_and_prepare(<span class="hljs-string">&quot;./output_dir&quot;</span>, file_format=<span class="hljs-string">&quot;parquet&quot;</span>)`}}),{c(){l=o("p"),_=c("Download and prepare the dataset as sharded Parquet files locally:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Download and prepare the dataset as sharded Parquet files locally:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function fg(k){let l,_,f,n,u;return n=new I({props:{code:`from datasets import load_dataset_builder
storage_options = {"key": aws_access_key_id, "secret": aws_secret_access_key}
builder = load_dataset_builder("rotten_tomatoes")
ds = builder.download_and_prepare("s3://my-bucket/my_rotten_tomatoes", storage_options=storage_options, file_format="parquet")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset_builder
<span class="hljs-meta">&gt;&gt;&gt; </span>storage_options = {<span class="hljs-string">&quot;key&quot;</span>: aws_access_key_id, <span class="hljs-string">&quot;secret&quot;</span>: aws_secret_access_key}
<span class="hljs-meta">&gt;&gt;&gt; </span>builder = load_dataset_builder(<span class="hljs-string">&quot;rotten_tomatoes&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = builder.download_and_prepare(<span class="hljs-string">&quot;s3://my-bucket/my_rotten_tomatoes&quot;</span>, storage_options=storage_options, file_format=<span class="hljs-string">&quot;parquet&quot;</span>)`}}),{c(){l=o("p"),_=c("Download and prepare the dataset as sharded Parquet files in a cloud storage:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Download and prepare the dataset as sharded Parquet files in a cloud storage:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function ug(k){let l,_,f,n,u;return n=new I({props:{code:`from datasets import load_dataset_builder
ds_builder = load_dataset_builder('rotten_tomatoes')
ds_builder.get_all_exported_dataset_infos()
`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset_builder
<span class="hljs-meta">&gt;&gt;&gt; </span>ds_builder = load_dataset_builder(<span class="hljs-string">&#x27;rotten_tomatoes&#x27;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds_builder.get_all_exported_dataset_infos()
{<span class="hljs-string">&#x27;default&#x27;</span>: DatasetInfo(description=<span class="hljs-string">&quot;Movie Review Dataset.
a dataset of containing 5,331 positive and 5,331 negative processed
s from Rotten Tomatoes movie reviews. This data was first used in Bo
 Lillian Lee, \`\`Seeing stars: Exploiting class relationships for
t categorization with respect to rating scales.&#x27;&#x27;, Proceedings of the
5.
ion=&#x27;@InProceedings{Pang+Lee:05a,
 =       {Bo Pang and Lillian Lee},
=        {Seeing stars: Exploiting class relationships for sentiment
          categorization with respect to rating scales},
tle =    {Proceedings of the ACL},
         2005

age=&#x27;http://www.cs.cornell.edu/people/pabo/movie-review-data/&#x27;, license=&#x27;&#x27;, features={&#x27;text&#x27;: Value(dtype=&#x27;string&#x27;, id=None), &#x27;label&#x27;: ClassLabel(num_classes=2, names=[&#x27;neg&#x27;, &#x27;pos&#x27;], id=None)}, post_processed=None, supervised_keys=SupervisedKeysData(input=&#x27;&#x27;, output=&#x27;&#x27;), task_templates=[TextClassification(task=&#x27;text-classification&#x27;, text_column=&#x27;text&#x27;, label_column=&#x27;label&#x27;)], builder_name=&#x27;rotten_tomatoes_movie_review&#x27;, config_name=&#x27;default&#x27;, version=1.0.0, splits={&#x27;train&#x27;: SplitInfo(name=&#x27;train&#x27;, num_bytes=1074810, num_examples=8530, dataset_name=&#x27;rotten_tomatoes_movie_review&#x27;), &#x27;validation&#x27;: SplitInfo(name=&#x27;validation&#x27;, num_bytes=134679, num_examples=1066, dataset_name=&#x27;rotten_tomatoes_movie_review&#x27;), &#x27;test&#x27;: SplitInfo(name=&#x27;test&#x27;, num_bytes=135972, num_examples=1066, dataset_name=&#x27;rotten_tomatoes_movie_review&#x27;)}, download_checksums={&#x27;https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz&#x27;: {&#x27;num_bytes&#x27;: 487770, &#x27;checksum&#x27;: &#x27;a05befe52aafda71d458d188a1c54506a998b1308613ba76bbda2e5029409ce9&#x27;}}, download_size=487770, post_processing_size=None, dataset_size=1345461, size_in_bytes=1833231)}</span>`}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function _g(k){let l,_,f,n,u;return n=new I({props:{code:`from datasets import load_dataset_builder
ds_builder = load_dataset_builder('rotten_tomatoes')
ds_builder.get_exported_dataset_info()
`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset_builder
<span class="hljs-meta">&gt;&gt;&gt; </span>ds_builder = load_dataset_builder(<span class="hljs-string">&#x27;rotten_tomatoes&#x27;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds_builder.get_exported_dataset_info()
DatasetInfo(description=<span class="hljs-string">&quot;Movie Review Dataset.
a dataset of containing 5,331 positive and 5,331 negative processed
s from Rotten Tomatoes movie reviews. This data was first used in Bo
 Lillian Lee, \`\`Seeing stars: Exploiting class relationships for
t categorization with respect to rating scales.&#x27;&#x27;, Proceedings of the
5.
ion=&#x27;@InProceedings{Pang+Lee:05a,
 =       {Bo Pang and Lillian Lee},
=        {Seeing stars: Exploiting class relationships for sentiment
          categorization with respect to rating scales},
tle =    {Proceedings of the ACL},
         2005

age=&#x27;http://www.cs.cornell.edu/people/pabo/movie-review-data/&#x27;, license=&#x27;&#x27;, features={&#x27;text&#x27;: Value(dtype=&#x27;string&#x27;, id=None), &#x27;label&#x27;: ClassLabel(num_classes=2, names=[&#x27;neg&#x27;, &#x27;pos&#x27;], id=None)}, post_processed=None, supervised_keys=SupervisedKeysData(input=&#x27;&#x27;, output=&#x27;&#x27;), task_templates=[TextClassification(task=&#x27;text-classification&#x27;, text_column=&#x27;text&#x27;, label_column=&#x27;label&#x27;)], builder_name=&#x27;rotten_tomatoes_movie_review&#x27;, config_name=&#x27;default&#x27;, version=1.0.0, splits={&#x27;train&#x27;: SplitInfo(name=&#x27;train&#x27;, num_bytes=1074810, num_examples=8530, dataset_name=&#x27;rotten_tomatoes_movie_review&#x27;), &#x27;validation&#x27;: SplitInfo(name=&#x27;validation&#x27;, num_bytes=134679, num_examples=1066, dataset_name=&#x27;rotten_tomatoes_movie_review&#x27;), &#x27;test&#x27;: SplitInfo(name=&#x27;test&#x27;, num_bytes=135972, num_examples=1066, dataset_name=&#x27;rotten_tomatoes_movie_review&#x27;)}, download_checksums={&#x27;https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz&#x27;: {&#x27;num_bytes&#x27;: 487770, &#x27;checksum&#x27;: &#x27;a05befe52aafda71d458d188a1c54506a998b1308613ba76bbda2e5029409ce9&#x27;}}, download_size=487770, post_processing_size=None, dataset_size=1345461, size_in_bytes=1833231)</span>`}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function hg(k){let l,_,f,n,u;return n=new I({props:{code:"downloaded_files = dl_manager.download('https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz')",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>downloaded_files = dl_manager.download(<span class="hljs-string">&#x27;https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz&#x27;</span>)'}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function $g(k){let l,_,f,n,u;return n=new I({props:{code:"extracted_paths = dl_manager.extract(dl_manager.download(url_or_urls))",highlighted:'<span class="hljs-attr">extracted_paths</span> = dl_manager.extract(dl_manager.download(url_or_urls))'}}),{c(){l=o("p"),_=c("Is roughly equivalent to:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Is roughly equivalent to:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function bg(k){let l,_,f,n,u;return n=new I({props:{code:"downloaded_files = dl_manager.download_custom('s3://my-bucket/data.zip', custom_download_for_my_private_bucket)",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>downloaded_files = dl_manager.download_custom(<span class="hljs-string">&#x27;s3://my-bucket/data.zip&#x27;</span>, custom_download_for_my_private_bucket)'}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function vg(k){let l,_,f,n,u;return n=new I({props:{code:`downloaded_files = dl_manager.download('https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz')
extracted_files = dl_manager.extract(downloaded_files)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>downloaded_files = dl_manager.download(<span class="hljs-string">&#x27;https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz&#x27;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>extracted_files = dl_manager.extract(downloaded_files)`}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function wg(k){let l,_,f,n,u;return n=new I({props:{code:`archive = dl_manager.download('https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz')
files = dl_manager.iter_archive(archive)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>archive = dl_manager.download(<span class="hljs-string">&#x27;https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz&#x27;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>files = dl_manager.iter_archive(archive)`}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function xg(k){let l,_,f,n,u;return n=new I({props:{code:`files = dl_manager.download_and_extract('https://huggingface.co/datasets/beans/resolve/main/data/train.zip')
files = dl_manager.iter_files(files)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>files = dl_manager.download_and_extract(<span class="hljs-string">&#x27;https://huggingface.co/datasets/beans/resolve/main/data/train.zip&#x27;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>files = dl_manager.iter_files(files)`}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function Dg(k){let l,_,f,n,u;return n=new I({props:{code:"downloaded_files = dl_manager.download('https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz')",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>downloaded_files = dl_manager.download(<span class="hljs-string">&#x27;https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz&#x27;</span>)'}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function Eg(k){let l,_,f,n,u;return n=new I({props:{code:"urls = dl_manager.extract(dl_manager.download(url_or_urls))",highlighted:'<span class="hljs-attribute">urls</span> <span class="hljs-operator">=</span> dl_manager.extract(dl_manager.download(url_or_urls))'}}),{c(){l=o("p"),_=c("Is equivalent to:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Is equivalent to:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function yg(k){let l,_,f,n,u;return n=new I({props:{code:`downloaded_files = dl_manager.download('https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz')
extracted_files = dl_manager.extract(downloaded_files)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>downloaded_files = dl_manager.download(<span class="hljs-string">&#x27;https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz&#x27;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>extracted_files = dl_manager.extract(downloaded_files)`}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function kg(k){let l,_,f,n,u;return n=new I({props:{code:`archive = dl_manager.download('https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz')
files = dl_manager.iter_archive(archive)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>archive = dl_manager.download(<span class="hljs-string">&#x27;https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz&#x27;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>files = dl_manager.iter_archive(archive)`}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function jg(k){let l,_,f,n,u;return n=new I({props:{code:`files = dl_manager.download_and_extract('https://huggingface.co/datasets/beans/resolve/main/data/train.zip')
files = dl_manager.iter_files(files)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>files = dl_manager.download_and_extract(<span class="hljs-string">&#x27;https://huggingface.co/datasets/beans/resolve/main/data/train.zip&#x27;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>files = dl_manager.iter_files(files)`}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function Tg(k){let l,_,f,n,u;return n=new I({props:{code:`datasets.SplitGenerator(
    name=datasets.Split.TRAIN,
    gen_kwargs={"split_key": "train", "files": dl_manager.download_and_extract(url)},
)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>datasets.SplitGenerator(
<span class="hljs-meta">... </span>    name=datasets.Split.TRAIN,
<span class="hljs-meta">... </span>    gen_kwargs={<span class="hljs-string">&quot;split_key&quot;</span>: <span class="hljs-string">&quot;train&quot;</span>, <span class="hljs-string">&quot;files&quot;</span>: dl_manager.download_and_extract(url)},
<span class="hljs-meta">... </span>)`}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function Sg(k){let l,_,f,n,u;return n=new I({props:{code:`datasets.SplitGenerator(
    name=datasets.Split.TRAIN,
    gen_kwargs={"split_key": "train", "files": dl_manager.download_and extract(url)},
),
datasets.SplitGenerator(
    name=datasets.Split.VALIDATION,
    gen_kwargs={"split_key": "validation", "files": dl_manager.download_and extract(url)},
),
datasets.SplitGenerator(
    name=datasets.Split.TEST,
    gen_kwargs={"split_key": "test", "files": dl_manager.download_and extract(url)},
)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>datasets.SplitGenerator(
<span class="hljs-meta">... </span>    name=datasets.Split.TRAIN,
<span class="hljs-meta">... </span>    gen_kwargs={<span class="hljs-string">&quot;split_key&quot;</span>: <span class="hljs-string">&quot;train&quot;</span>, <span class="hljs-string">&quot;files&quot;</span>: dl_manager.download_and extract(url)},
<span class="hljs-meta">... </span>),
<span class="hljs-meta">... </span>datasets.SplitGenerator(
<span class="hljs-meta">... </span>    name=datasets.Split.VALIDATION,
<span class="hljs-meta">... </span>    gen_kwargs={<span class="hljs-string">&quot;split_key&quot;</span>: <span class="hljs-string">&quot;validation&quot;</span>, <span class="hljs-string">&quot;files&quot;</span>: dl_manager.download_and extract(url)},
<span class="hljs-meta">... </span>),
<span class="hljs-meta">... </span>datasets.SplitGenerator(
<span class="hljs-meta">... </span>    name=datasets.Split.TEST,
<span class="hljs-meta">... </span>    gen_kwargs={<span class="hljs-string">&quot;split_key&quot;</span>: <span class="hljs-string">&quot;test&quot;</span>, <span class="hljs-string">&quot;files&quot;</span>: dl_manager.download_and extract(url)},
<span class="hljs-meta">... </span>)`}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function Ig(k){let l,_,f,n,u;return n=new I({props:{code:"split = datasets.Split.TRAIN.subsplit(datasets.percent[0:25]) + datasets.Split.TEST",highlighted:'split = datasets.Split.TRAIN.subsplit(datasets.percent[<span class="hljs-number">0</span>:<span class="hljs-number">25</span>]) + datasets.Split.TEST'}}),{c(){l=o("p"),_=c("Each descriptor can be composed with other using addition or slice:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Each descriptor can be composed with other using addition or slice:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function Bg(k){let l,_,f,n,u;return n=new I({props:{code:`split = (
        datasets.Split.TRAIN.subsplit(datasets.percent[:25]) +
        datasets.Split.TRAIN.subsplit(datasets.percent[75:])
)  # Error
split = datasets.Split.TEST + datasets.Split.ALL  # Error`,highlighted:`split = (
        datasets.Split.TRAIN.subsplit(datasets.percent[:<span class="hljs-number">25</span>]) +
        datasets.Split.TRAIN.subsplit(datasets.percent[<span class="hljs-number">75</span>:])
)  <span class="hljs-comment"># Error</span>
split = datasets.Split.TEST + datasets.Split.ALL  <span class="hljs-comment"># Error</span>`}}),{c(){l=o("p"),_=c("A split cannot be added twice, so the following will fail:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"A split cannot be added twice, so the following will fail:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function Ng(k){let l,_,f,n,u;return n=new I({props:{code:`split = (
        datasets.Split.TRAIN.subsplit(datasets.percent[:25]) +
        datasets.Split.TEST.subsplit(datasets.percent[:50])
)
split = (datasets.Split.TRAIN + datasets.Split.TEST).subsplit(datasets.percent[:50])`,highlighted:`split = (
        datasets.Split.TRAIN.subsplit(datasets.percent[:<span class="hljs-number">25</span>]) +
        datasets.Split.TEST.subsplit(datasets.percent[:<span class="hljs-number">50</span>])
)
split = (datasets.Split.TRAIN + datasets.Split.TEST).subsplit(datasets.percent[:<span class="hljs-number">50</span>])`}}),{c(){l=o("p"),_=c("The slices can be applied only one time. So the following are valid:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"The slices can be applied only one time. So the following are valid:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function Pg(k){let l,_,f,n,u;return n=new I({props:{code:`train = datasets.Split.TRAIN
test = datasets.Split.TEST
split = train.subsplit(datasets.percent[:25]).subsplit(datasets.percent[:25])
split = (train.subsplit(datasets.percent[:25]) + test).subsplit(datasets.percent[:50])`,highlighted:`train = datasets.Split.TRAIN
test = datasets.Split.TEST
split = train.subsplit(datasets.percent[:<span class="hljs-number">25</span>]).subsplit(datasets.percent[:<span class="hljs-number">25</span>])
split = (train.subsplit(datasets.percent[:<span class="hljs-number">25</span>]) + test).subsplit(datasets.percent[:<span class="hljs-number">50</span>])`}}),{c(){l=o("p"),_=c("But this is not valid:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"But this is not valid:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function Cg(k){let l,_,f,n,u;return n=new I({props:{code:`# The following lines are equivalent:
ds = datasets.load_dataset('mnist', split='test[:33%]')
ds = datasets.load_dataset('mnist', split=datasets.ReadInstruction.from_spec('test[:33%]'))
ds = datasets.load_dataset('mnist', split=datasets.ReadInstruction('test', to=33, unit='%'))
ds = datasets.load_dataset('mnist', split=datasets.ReadInstruction(
'test', from_=0, to=33, unit='%'))

# The following lines are equivalent:
ds = datasets.load_dataset('mnist', split='test[:33%]+train[1:-1]')
ds = datasets.load_dataset('mnist', split=datasets.ReadInstruction.from_spec(
'test[:33%]+train[1:-1]'))
ds = datasets.load_dataset('mnist', split=(
datasets.ReadInstruction('test', to=33, unit='%') +
datasets.ReadInstruction('train', from_=1, to=-1, unit='abs')))

# The following lines are equivalent:
ds = datasets.load_dataset('mnist', split='test[:33%](pct1_dropremainder)')
ds = datasets.load_dataset('mnist', split=datasets.ReadInstruction.from_spec(
'test[:33%](pct1_dropremainder)'))
ds = datasets.load_dataset('mnist', split=datasets.ReadInstruction(
'test', from_=0, to=33, unit='%', rounding="pct1_dropremainder"))

# 10-fold validation:
tests = datasets.load_dataset(
'mnist',
[datasets.ReadInstruction('train', from_=k, to=k+10, unit='%')
for k in range(0, 100, 10)])
trains = datasets.load_dataset(
'mnist',
[datasets.ReadInstruction('train', to=k, unit='%') + datasets.ReadInstruction('train', from_=k+10, unit='%')
for k in range(0, 100, 10)])`,highlighted:`<span class="hljs-comment"># The following lines are equivalent:</span>
ds = datasets.load_dataset(<span class="hljs-string">&#x27;mnist&#x27;</span>, split=<span class="hljs-string">&#x27;test[:33%]&#x27;</span>)
ds = datasets.load_dataset(<span class="hljs-string">&#x27;mnist&#x27;</span>, split=datasets.ReadInstruction.from_spec(<span class="hljs-string">&#x27;test[:33%]&#x27;</span>))
ds = datasets.load_dataset(<span class="hljs-string">&#x27;mnist&#x27;</span>, split=datasets.ReadInstruction(<span class="hljs-string">&#x27;test&#x27;</span>, to=<span class="hljs-number">33</span>, unit=<span class="hljs-string">&#x27;%&#x27;</span>))
ds = datasets.load_dataset(<span class="hljs-string">&#x27;mnist&#x27;</span>, split=datasets.ReadInstruction(
<span class="hljs-string">&#x27;test&#x27;</span>, from_=<span class="hljs-number">0</span>, to=<span class="hljs-number">33</span>, unit=<span class="hljs-string">&#x27;%&#x27;</span>))

<span class="hljs-comment"># The following lines are equivalent:</span>
ds = datasets.load_dataset(<span class="hljs-string">&#x27;mnist&#x27;</span>, split=<span class="hljs-string">&#x27;test[:33%]+train[1:-1]&#x27;</span>)
ds = datasets.load_dataset(<span class="hljs-string">&#x27;mnist&#x27;</span>, split=datasets.ReadInstruction.from_spec(
<span class="hljs-string">&#x27;test[:33%]+train[1:-1]&#x27;</span>))
ds = datasets.load_dataset(<span class="hljs-string">&#x27;mnist&#x27;</span>, split=(
datasets.ReadInstruction(<span class="hljs-string">&#x27;test&#x27;</span>, to=<span class="hljs-number">33</span>, unit=<span class="hljs-string">&#x27;%&#x27;</span>) +
datasets.ReadInstruction(<span class="hljs-string">&#x27;train&#x27;</span>, from_=<span class="hljs-number">1</span>, to=-<span class="hljs-number">1</span>, unit=<span class="hljs-string">&#x27;abs&#x27;</span>)))

<span class="hljs-comment"># The following lines are equivalent:</span>
ds = datasets.load_dataset(<span class="hljs-string">&#x27;mnist&#x27;</span>, split=<span class="hljs-string">&#x27;test[:33%](pct1_dropremainder)&#x27;</span>)
ds = datasets.load_dataset(<span class="hljs-string">&#x27;mnist&#x27;</span>, split=datasets.ReadInstruction.from_spec(
<span class="hljs-string">&#x27;test[:33%](pct1_dropremainder)&#x27;</span>))
ds = datasets.load_dataset(<span class="hljs-string">&#x27;mnist&#x27;</span>, split=datasets.ReadInstruction(
<span class="hljs-string">&#x27;test&#x27;</span>, from_=<span class="hljs-number">0</span>, to=<span class="hljs-number">33</span>, unit=<span class="hljs-string">&#x27;%&#x27;</span>, rounding=<span class="hljs-string">&quot;pct1_dropremainder&quot;</span>))

<span class="hljs-comment"># 10-fold validation:</span>
tests = datasets.load_dataset(
<span class="hljs-string">&#x27;mnist&#x27;</span>,
[datasets.ReadInstruction(<span class="hljs-string">&#x27;train&#x27;</span>, from_=k, to=k+<span class="hljs-number">10</span>, unit=<span class="hljs-string">&#x27;%&#x27;</span>)
<span class="hljs-keyword">for</span> k <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">0</span>, <span class="hljs-number">100</span>, <span class="hljs-number">10</span>)])
trains = datasets.load_dataset(
<span class="hljs-string">&#x27;mnist&#x27;</span>,
[datasets.ReadInstruction(<span class="hljs-string">&#x27;train&#x27;</span>, to=k, unit=<span class="hljs-string">&#x27;%&#x27;</span>) + datasets.ReadInstruction(<span class="hljs-string">&#x27;train&#x27;</span>, from_=k+<span class="hljs-number">10</span>, unit=<span class="hljs-string">&#x27;%&#x27;</span>)
<span class="hljs-keyword">for</span> k <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">0</span>, <span class="hljs-number">100</span>, <span class="hljs-number">10</span>)])`}}),{c(){l=o("p"),_=c("Examples:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Examples:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function Rg(k){let l,_,f,n,u;return n=new I({props:{code:`test: test split.
test + validation: test split + validation split.
test[10:]: test split, minus its first 10 records.
test[:10%]: first 10% records of test split.
test[:20%](pct1_dropremainder): first 10% records, rounded with the pct1_dropremainder rounding.
test[:-5%]+train[40%:60%]: first 95% of test + middle 20% of train.`,highlighted:`<span class="hljs-keyword">test: test</span> split.
<span class="hljs-keyword">test </span>+ validation: test split + validation split.
test[10:]: test split, minus its first 10 records.
test[:10%]: first 10% records of test split.
test[:20%](pct1_dropremainder): first 10% records, rounded with the pct1_dropremainder rounding.
test[:<span class="hljs-string">-5</span>%]+train[40%:60%]: first 95% of test + middle 20% of train.`}}),{c(){l=o("p"),_=c("Examples:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Examples:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function Lg(k){let l,_,f,n,u;return n=new I({props:{code:'VERSION = datasets.Version("1.0.0")',highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>VERSION = datasets.Version(<span class="hljs-string">&quot;1.0.0&quot;</span>)'}}),{c(){l=o("p"),_=c("Example:"),f=m(),b(n.$$.fragment)},l(t){l=r(t,"P",{});var i=d(l);_=p(i,"Example:"),i.forEach(a),f=g(t),v(n.$$.fragment,t)},m(t,i){$(t,l,i),e(l,_),$(t,f,i),w(n,t,i),u=!0},p:S,i(t){u||(x(n.$$.fragment,t),u=!0)},o(t){D(n.$$.fragment,t),u=!1},d(t){t&&a(l),t&&a(f),E(n,t)}}}function Ag(k){let l,_,f,n,u,t,i,de,R,H,K,Ue,ws,It,fn,xs,un,xr,le,_n,Pa,hn,$n,Ca,bn,vn,Dr,B,Bt,wn,Ds,xn,Dn,Ra,Es,En,yn,kn,ye,La,ys,jn,Tn,Sn,Aa,Oa,In,Bn,Nn,ze,qa,Pn,Cn,Ma,Rn,Ln,An,ae,On,ks,qn,Mn,Va,Vn,Un,js,zn,Fn,Gn,ie,Nt,Hn,Ts,Wn,Xn,Fe,Kn,O,Pt,Jn,Ss,Yn,Qn,Is,Zn,ed,Ge,td,He,ad,We,sd,ce,Ct,od,Bs,rd,nd,Xe,dd,pe,Rt,ld,Lt,id,Ns,cd,pd,md,Ke,gd,Je,At,fd,Ps,ud,Er,se,Ot,_d,Cs,hd,$d,me,Rs,bd,vd,Ls,wd,xd,As,Dd,Ed,yr,ke,qt,yd,Os,kd,kr,je,Mt,jd,qs,Td,jr,W,Vt,Sd,Ut,Id,Ms,Bd,Nd,Pd,Ye,Vs,Cd,Rd,Us,Ld,Ad,Od,J,zt,qd,zs,Md,Vd,Te,Fs,Ud,zd,Gs,Fd,Gd,Hs,Hd,Wd,Ws,Xd,Tr,Se,Qe,Xs,Ft,Kd,Ks,Jd,Sr,P,Gt,Yd,Y,Ht,Qd,Js,Zd,el,Wt,tl,Ys,al,sl,ol,Ze,rl,ge,Xt,nl,Kt,dl,Qs,ll,il,cl,et,pl,fe,Jt,ml,Yt,gl,Zs,fl,ul,_l,tt,hl,ue,Qt,$l,eo,bl,vl,at,wl,_e,Zt,xl,to,Dl,El,st,yl,he,ea,kl,ao,jl,Tl,ot,Sl,rt,ta,Il,so,Bl,Ir,L,aa,Nl,V,Pl,oo,Cl,Rl,ro,Ll,Al,no,Ol,ql,lo,Ml,Vl,io,Ul,zl,Fl,$e,sa,Gl,oa,Hl,co,Wl,Xl,Kl,nt,Jl,Q,ra,Yl,na,Ql,po,Zl,ei,ti,da,ai,mo,si,oi,ri,dt,ni,Z,la,di,go,li,ii,ia,ci,fo,pi,mi,gi,lt,fi,be,ca,ui,uo,_i,hi,it,$i,ve,pa,bi,_o,vi,wi,ct,Br,Ie,ma,xi,ho,Di,Nr,U,ga,Ei,Ua,$o,yi,ki,ji,fa,Ti,bo,Si,Ii,Bi,vo,Ni,Pi,ua,wo,Be,Pr,Ci,xo,Ri,Li,Do,Ai,Oi,Ne,Pe,za,Eo,qi,Mi,Vi,yo,Ui,zi,ko,Fi,Gi,Ce,jo,To,Hi,Wi,So,Xi,Ki,Io,Ji,Yi,Re,Bo,No,Qi,Zi,Po,ec,tc,Co,ac,Cr,Le,pt,Ro,_a,sc,Lo,oc,Rr,X,ha,rc,Ao,nc,dc,Ae,lc,Oo,ic,cc,qo,pc,mc,gc,mt,Lr,A,$a,fc,Fa,Mo,uc,_c,hc,Vo,$c,bc,oe,Ga,Uo,vc,wc,xc,Ha,zo,Dc,Ec,yc,Wa,Fo,kc,jc,Tc,Xa,Go,Sc,Ic,Bc,ba,Nc,Ho,Pc,Cc,Rc,va,Lc,Ka,Ac,Oc,qc,gt,Ar,C,wa,Mc,Wo,Vc,Uc,Xo,zc,Fc,ft,Gc,Ko,Hc,Wc,ut,Xc,_t,Kc,ht,Or,Oe,xa,Jc,Jo,Yc,qr,z,Da,Qc,Yo,Zc,ep,$t,tp,we,Ea,ap,ya,sp,Qo,op,rp,np,bt,dp,xe,ka,lp,Zo,ip,cp,er,pp,Mr,qe,vt,tr,ja,mp,ar,gp,Vr,re,Ta,fp,Sa,up,sr,_p,hp,$p,wt,Ur;return t=new wr({}),It=new wr({}),Bt=new j({props:{name:"class datasets.DatasetBuilder",anchor:"datasets.DatasetBuilder",parameters:[{name:"cache_dir",val:": typing.Optional[str] = None"},{name:"config_name",val:": typing.Optional[str] = None"},{name:"hash",val:": typing.Optional[str] = None"},{name:"base_path",val:": typing.Optional[str] = None"},{name:"info",val:": typing.Optional[datasets.info.DatasetInfo] = None"},{name:"features",val:": typing.Optional[datasets.features.features.Features] = None"},{name:"use_auth_token",val:": typing.Union[str, bool, NoneType] = None"},{name:"repo_id",val:": typing.Optional[str] = None"},{name:"data_files",val:": typing.Union[str, list, dict, datasets.data_files.DataFilesDict, NoneType] = None"},{name:"data_dir",val:": typing.Optional[str] = None"},{name:"name",val:" = 'deprecated'"},{name:"**config_kwargs",val:""}],parametersDescription:[{anchor:"datasets.DatasetBuilder.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Directory to cache data. Defaults to <code>&quot;~/.cache/huggingface/datasets&quot;</code>.`,name:"cache_dir"},{anchor:"datasets.DatasetBuilder.config_name",description:`<strong>config_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Name of the dataset configuration.
It affects the data generated on disk. Different configurations will have their own subdirectories and
versions.
If not provided, the default configuration is used (if it exists).</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						<p class="font-medium">Added in 2.3.0</p>
						
<p>Parameter <code>name</code> was renamed to <code>config_name</code>.</p>

					</div>`,name:"config_name"},{anchor:"datasets.DatasetBuilder.hash",description:`<strong>hash</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Hash specific to the dataset code. Used to update the caching directory when the
dataset loading script code is updated (to avoid reusing old data).
The typical caching directory (defined in <code>self._relative_data_dir</code>) is <code>name/version/hash/</code>.`,name:"hash"},{anchor:"datasets.DatasetBuilder.base_path",description:`<strong>base_path</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Base path for relative paths that are used to download files.
This can be a remote URL.`,name:"base_path"},{anchor:"datasets.DatasetBuilder.features",description:`<strong>features</strong> (<a href="/docs/datasets/v2.8.0/en/package_reference/main_classes#datasets.Features">Features</a>, <em>optional</em>) &#x2014;
Features types to use with this dataset.
It can be used to change the <a href="/docs/datasets/v2.8.0/en/package_reference/main_classes#datasets.Features">Features</a> types of a dataset, for example.`,name:"features"},{anchor:"datasets.DatasetBuilder.use_auth_token",description:`<strong>use_auth_token</strong> (<code>str</code> or <code>bool</code>, <em>optional</em>) &#x2014;
String or boolean to use as Bearer token for remote files on the
Datasets Hub. If <code>True</code>, will get token from <code>&quot;~/.huggingface&quot;</code>.`,name:"use_auth_token"},{anchor:"datasets.DatasetBuilder.repo_id",description:`<strong>repo_id</strong> (<code>str</code>, <em>optional</em>) &#x2014;
ID of the dataset repository.
Used to distinguish builders with the same name but not coming from the same namespace, for example &#x201C;squad&#x201D;
and &#x201C;lhoestq/squad&#x201D; repo IDs. In the latter, the builder name would be &#x201C;lhoestq___squad&#x201D;.`,name:"repo_id"},{anchor:"datasets.DatasetBuilder.data_files",description:`<strong>data_files</strong> (<code>str</code> or <code>Sequence</code> or <code>Mapping</code>, <em>optional</em>) &#x2014;
Path(s) to source data file(s).
For builders like &#x201C;csv&#x201D; or &#x201C;json&#x201D; that need the user to specify data files. They can be either
local or remote files. For convenience, you can use a <code>DataFilesDict</code>.`,name:"data_files"},{anchor:"datasets.DatasetBuilder.data_dir",description:`<strong>data_dir</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Path to directory containing source data file(s).
Use only if <code>data_files</code> is not passed, in which case it is equivalent to passing
<code>os.path.join(data_dir, &quot;**&quot;)</code> as <code>data_files</code>.
For builders that require manual download, it must be the path to the local directory containing the
manually downloaded data.`,name:"data_dir"},{anchor:"datasets.DatasetBuilder.name",description:`<strong>name</strong> (<code>str</code>) &#x2014; Configuration name for the dataset.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
						<p class="font-medium">Deprecated in 2.3.0</p>
						
<p>Use <code>config_name</code> instead.</p>

					</div>`,name:"name"},{anchor:"datasets.DatasetBuilder.*config_kwargs",description:`*<strong>*config_kwargs</strong> (additional keyword arguments) &#x2014; Keyword arguments to be passed to the corresponding builder
configuration class, set on the class attribute <a href="/docs/datasets/v2.8.0/en/package_reference/builder_classes#datasets.BuilderConfig">DatasetBuilder.BUILDER_CONFIG_CLASS</a>. The builder
configuration class is <a href="/docs/datasets/v2.8.0/en/package_reference/builder_classes#datasets.BuilderConfig">BuilderConfig</a> or a subclass of it.`,name:"*config_kwargs"}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/builder.py#L197"}}),Nt=new j({props:{name:"as_dataset",anchor:"datasets.DatasetBuilder.as_dataset",parameters:[{name:"split",val:": typing.Optional[datasets.splits.Split] = None"},{name:"run_post_process",val:" = True"},{name:"ignore_verifications",val:" = False"},{name:"in_memory",val:" = False"}],parametersDescription:[{anchor:"datasets.DatasetBuilder.as_dataset.split",description:`<strong>split</strong> (<code>datasets.Split</code>) &#x2014;
Which subset of the data to return.`,name:"split"},{anchor:"datasets.DatasetBuilder.as_dataset.run_post_process",description:`<strong>run_post_process</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether to run post-processing dataset transforms and/or add
indexes.`,name:"run_post_process"},{anchor:"datasets.DatasetBuilder.as_dataset.ignore_verifications",description:`<strong>ignore_verifications</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Whether to ignore the verifications of the
downloaded/processed dataset information (checksums/size/splits/&#x2026;).`,name:"ignore_verifications"},{anchor:"datasets.DatasetBuilder.as_dataset.in_memory",description:`<strong>in_memory</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Whether to copy the data in-memory.`,name:"in_memory"}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/builder.py#L1015",returnDescription:`
<p>datasets.Dataset</p>
`}}),Fe=new T({props:{anchor:"datasets.DatasetBuilder.as_dataset.example",$$slots:{default:[pg]},$$scope:{ctx:k}}}),Pt=new j({props:{name:"download_and_prepare",anchor:"datasets.DatasetBuilder.download_and_prepare",parameters:[{name:"output_dir",val:": typing.Optional[str] = None"},{name:"download_config",val:": typing.Optional[datasets.download.download_config.DownloadConfig] = None"},{name:"download_mode",val:": typing.Optional[datasets.download.download_manager.DownloadMode] = None"},{name:"ignore_verifications",val:": bool = False"},{name:"try_from_hf_gcs",val:": bool = True"},{name:"dl_manager",val:": typing.Optional[datasets.download.download_manager.DownloadManager] = None"},{name:"base_path",val:": typing.Optional[str] = None"},{name:"use_auth_token",val:" = 'deprecated'"},{name:"file_format",val:": str = 'arrow'"},{name:"max_shard_size",val:": typing.Union[str, int, NoneType] = None"},{name:"num_proc",val:": typing.Optional[int] = None"},{name:"storage_options",val:": typing.Optional[dict] = None"},{name:"**download_and_prepare_kwargs",val:""}],parametersDescription:[{anchor:"datasets.DatasetBuilder.download_and_prepare.output_dir",description:`<strong>output_dir</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Output directory for the dataset.
Default to this builder&#x2019;s <code>cache_dir</code>, which is inside <code>~/.cache/huggingface/datasets</code> by default.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						<p class="font-medium">Added in 2.5.0</p>
						
					</div>`,name:"output_dir"},{anchor:"datasets.DatasetBuilder.download_and_prepare.download_config",description:`<strong>download_config</strong> (<code>DownloadConfig</code>, <em>optional</em>) &#x2014;
Specific download configuration parameters.`,name:"download_config"},{anchor:"datasets.DatasetBuilder.download_and_prepare.download_mode",description:`<strong>download_mode</strong> (<code>DownloadMode</code>, <em>optional</em>) &#x2014;
Select the download/generate mode, default to <code>REUSE_DATASET_IF_EXISTS</code>.`,name:"download_mode"},{anchor:"datasets.DatasetBuilder.download_and_prepare.ignore_verifications",description:`<strong>ignore_verifications</strong> (<code>bool</code>) &#x2014;
Ignore the verifications of the downloaded/processed dataset information (checksums/size/splits/&#x2026;).`,name:"ignore_verifications"},{anchor:"datasets.DatasetBuilder.download_and_prepare.try_from_hf_gcs",description:`<strong>try_from_hf_gcs</strong> (<code>bool</code>) &#x2014;
If <code>True</code>, it will try to download the already prepared dataset from the HF Google cloud storage.`,name:"try_from_hf_gcs"},{anchor:"datasets.DatasetBuilder.download_and_prepare.dl_manager",description:`<strong>dl_manager</strong> (<code>DownloadManager</code>, <em>optional</em>) &#x2014;
Specific <code>DownloadManger</code> to use.`,name:"dl_manager"},{anchor:"datasets.DatasetBuilder.download_and_prepare.base_path",description:`<strong>base_path</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Base path for relative paths that are used to download files. This can be a remote url.
If not specified, the value of the <code>base_path</code> attribute (<code>self.base_path</code>) will be used instead.`,name:"base_path"},{anchor:"datasets.DatasetBuilder.download_and_prepare.use_auth_token",description:`<strong>use_auth_token</strong> (<code>Union[str, bool]</code>, <em>optional</em>) &#x2014;
Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
If True, or not specified, will get token from ~/.huggingface.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
						<p class="font-medium">Deprecated in 2.7.1</p>
						
<p>Pass <code>use_auth_token</code> to the initializer/<code>load_dataset_builder</code> instead.</p>

					</div>`,name:"use_auth_token"},{anchor:"datasets.DatasetBuilder.download_and_prepare.file_format",description:`<strong>file_format</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Format of the data files in which the dataset will be written.
Supported formats: &#x201C;arrow&#x201D;, &#x201C;parquet&#x201D;. Default to &#x201C;arrow&#x201D; format.
If the format is &#x201C;parquet&#x201D;, then image and audio data are embedded into the Parquet files instead of pointing to local files.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						<p class="font-medium">Added in 2.5.0</p>
						
					</div>`,name:"file_format"},{anchor:"datasets.DatasetBuilder.download_and_prepare.max_shard_size",description:`<strong>max_shard_size</strong> (<code>Union[str, int]</code>, <em>optional</em>) &#x2014;
Maximum number of bytes written per shard, default is &#x201C;500MB&#x201D;.
The size is based on uncompressed data size, so in practice your shard files may be smaller than
<code>max_shard_size</code> thanks to Parquet compression for example.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						<p class="font-medium">Added in 2.5.0</p>
						
					</div>`,name:"max_shard_size"},{anchor:"datasets.DatasetBuilder.download_and_prepare.num_proc",description:`<strong>num_proc</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Number of processes when downloading and generating the dataset locally.
Multiprocessing is disabled by default.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						<p class="font-medium">Added in 2.7.0</p>
						
					</div>`,name:"num_proc"},{anchor:"datasets.DatasetBuilder.download_and_prepare.storage_options",description:`<strong>storage_options</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Key/value pairs to be passed on to the caching file-system backend, if any.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						<p class="font-medium">Added in 2.5.0</p>
						
					</div>`,name:"storage_options"},{anchor:"datasets.DatasetBuilder.download_and_prepare.*download_and_prepare_kwargs",description:"*<strong>*download_and_prepare_kwargs</strong> (additional keyword arguments) &#x2014; Keyword arguments.",name:"*download_and_prepare_kwargs"}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/builder.py#L615"}}),Ge=new T({props:{anchor:"datasets.DatasetBuilder.download_and_prepare.example",$$slots:{default:[mg]},$$scope:{ctx:k}}}),He=new T({props:{anchor:"datasets.DatasetBuilder.download_and_prepare.example-2",$$slots:{default:[gg]},$$scope:{ctx:k}}}),We=new T({props:{anchor:"datasets.DatasetBuilder.download_and_prepare.example-3",$$slots:{default:[fg]},$$scope:{ctx:k}}}),Ct=new j({props:{name:"get_all_exported_dataset_infos",anchor:"datasets.DatasetBuilder.get_all_exported_dataset_infos",parameters:[],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/builder.py#L404"}}),Xe=new T({props:{anchor:"datasets.DatasetBuilder.get_all_exported_dataset_infos.example",$$slots:{default:[ug]},$$scope:{ctx:k}}}),Rt=new j({props:{name:"get_exported_dataset_info",anchor:"datasets.DatasetBuilder.get_exported_dataset_info",parameters:[],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/builder.py#L419"}}),Ke=new T({props:{anchor:"datasets.DatasetBuilder.get_exported_dataset_info.example",$$slots:{default:[_g]},$$scope:{ctx:k}}}),At=new j({props:{name:"get_imported_module_dir",anchor:"datasets.DatasetBuilder.get_imported_module_dir",parameters:[],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/builder.py#L602"}}),Ot=new j({props:{name:"class datasets.GeneratorBasedBuilder",anchor:"datasets.GeneratorBasedBuilder",parameters:[{name:"*args",val:""},{name:"writer_batch_size",val:" = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/builder.py#L1338"}}),qt=new j({props:{name:"class datasets.BeamBasedBuilder",anchor:"datasets.BeamBasedBuilder",parameters:[{name:"*args",val:""},{name:"beam_runner",val:" = None"},{name:"beam_options",val:" = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/builder.py#L1863"}}),Mt=new j({props:{name:"class datasets.ArrowBasedBuilder",anchor:"datasets.ArrowBasedBuilder",parameters:[{name:"cache_dir",val:": typing.Optional[str] = None"},{name:"config_name",val:": typing.Optional[str] = None"},{name:"hash",val:": typing.Optional[str] = None"},{name:"base_path",val:": typing.Optional[str] = None"},{name:"info",val:": typing.Optional[datasets.info.DatasetInfo] = None"},{name:"features",val:": typing.Optional[datasets.features.features.Features] = None"},{name:"use_auth_token",val:": typing.Union[str, bool, NoneType] = None"},{name:"repo_id",val:": typing.Optional[str] = None"},{name:"data_files",val:": typing.Union[str, list, dict, datasets.data_files.DataFilesDict, NoneType] = None"},{name:"data_dir",val:": typing.Optional[str] = None"},{name:"name",val:" = 'deprecated'"},{name:"**config_kwargs",val:""}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/builder.py#L1619"}}),Vt=new j({props:{name:"class datasets.BuilderConfig",anchor:"datasets.BuilderConfig",parameters:[{name:"name",val:": str = 'default'"},{name:"version",val:": typing.Union[str, datasets.utils.version.Version, NoneType] = 0.0.0"},{name:"data_dir",val:": typing.Optional[str] = None"},{name:"data_files",val:": typing.Optional[datasets.data_files.DataFilesDict] = None"},{name:"description",val:": typing.Optional[str] = None"}],parametersDescription:[{anchor:"datasets.BuilderConfig.name",description:"<strong>name</strong> (<code>str</code>, defaults to <code>default</code>) &#x2014;",name:"name"},{anchor:"datasets.BuilderConfig.version",description:"<strong>version</strong> (<code>Version</code> or <code>str</code>, <em>optional</em>) &#x2014;",name:"version"},{anchor:"datasets.BuilderConfig.data_dir",description:"<strong>data_dir</strong> (<code>str</code>, <em>optional</em>) &#x2014;",name:"data_dir"},{anchor:"datasets.BuilderConfig.data_files",description:`<strong>data_files</strong> (<code>str</code> or <code>Sequence</code> or <code>Mapping</code>, <em>optional</em>) &#x2014;
Path(s) to source data file(s).`,name:"data_files"},{anchor:"datasets.BuilderConfig.description",description:"<strong>description</strong> (<code>str</code>, <em>optional</em>) &#x2014;",name:"description"}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/builder.py#L100"}}),zt=new j({props:{name:"create_config_id",anchor:"datasets.BuilderConfig.create_config_id",parameters:[{name:"config_kwargs",val:": dict"},{name:"custom_features",val:": typing.Optional[datasets.features.features.Features] = None"}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/builder.py#L139"}}),Ft=new wr({}),Gt=new j({props:{name:"class datasets.DownloadManager",anchor:"datasets.DownloadManager",parameters:[{name:"dataset_name",val:": typing.Optional[str] = None"},{name:"data_dir",val:": typing.Optional[str] = None"},{name:"download_config",val:": typing.Optional[datasets.download.download_config.DownloadConfig] = None"},{name:"base_path",val:": typing.Optional[str] = None"},{name:"record_checksums",val:" = True"}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/download/download_manager.py#L150"}}),Ht=new j({props:{name:"download",anchor:"datasets.DownloadManager.download",parameters:[{name:"url_or_urls",val:""}],parametersDescription:[{anchor:"datasets.DownloadManager.download.url_or_urls",description:`<strong>url_or_urls</strong> (<code>str</code> or <code>list</code> or <code>dict</code>) &#x2014;
URL or <code>list</code> or <code>dict</code> of URLs to download. Each URL is a <code>str</code>.`,name:"url_or_urls"}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/download/download_manager.py#L304",returnDescription:`
<p>The downloaded paths matching the given input <code>url_or_urls</code>.</p>
`,returnType:`
<p><code>str</code> or <code>list</code> or <code>dict</code></p>
`}}),Ze=new T({props:{anchor:"datasets.DownloadManager.download.example",$$slots:{default:[hg]},$$scope:{ctx:k}}}),Xt=new j({props:{name:"download_and_extract",anchor:"datasets.DownloadManager.download_and_extract",parameters:[{name:"url_or_urls",val:""}],parametersDescription:[{anchor:"datasets.DownloadManager.download_and_extract.url_or_urls",description:`<strong>url_or_urls</strong> (<code>str</code> or <code>list</code> or <code>dict</code>) &#x2014;
URL or <code>list</code> or <code>dict</code> of URLs to download and extract. Each URL is a <code>str</code>.`,name:"url_or_urls"}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/download/download_manager.py#L452",returnDescription:`
<p><code>str</code>, extracted paths of given URL(s).</p>
`,returnType:`
<p>extracted_path(s)</p>
`}}),et=new T({props:{anchor:"datasets.DownloadManager.download_and_extract.example",$$slots:{default:[$g]},$$scope:{ctx:k}}}),Jt=new j({props:{name:"download_custom",anchor:"datasets.DownloadManager.download_custom",parameters:[{name:"url_or_urls",val:""},{name:"custom_download",val:""}],parametersDescription:[{anchor:"datasets.DownloadManager.download_custom.url_or_urls",description:`<strong>url_or_urls</strong> (<code>str</code> or <code>list</code> or <code>dict</code>) &#x2014;
URL or <code>list</code> or <code>dict</code> of URLs to download and extract. Each URL is a <code>str</code>.`,name:"url_or_urls"},{anchor:"datasets.DownloadManager.download_custom.custom_download",description:`<strong>custom_download</strong> (<code>Callable[src_url, dst_path]</code>) &#x2014;
The source URL and destination path. For example
<code>tf.io.gfile.copy</code>, that lets you download from  Google storage.`,name:"custom_download"}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/download/download_manager.py#L256",returnDescription:`
<p><code>str</code>, The downloaded paths matching the given input
<code>url_or_urls</code>.</p>
`,returnType:`
<p>downloaded_path(s)</p>
`}}),tt=new T({props:{anchor:"datasets.DownloadManager.download_custom.example",$$slots:{default:[bg]},$$scope:{ctx:k}}}),Qt=new j({props:{name:"extract",anchor:"datasets.DownloadManager.extract",parameters:[{name:"path_or_paths",val:""},{name:"num_proc",val:" = 'deprecated'"}],parametersDescription:[{anchor:"datasets.DownloadManager.extract.path_or_paths",description:`<strong>path_or_paths</strong> (path or <code>list</code> or <code>dict</code>) &#x2014;
Path of file to extract. Each path is a <code>str</code>.`,name:"path_or_paths"},{anchor:"datasets.DownloadManager.extract.num_proc",description:`<strong>num_proc</strong> (<code>int</code>) &#x2014;
Use multi-processing if <code>num_proc</code> &gt; 1 and the length of
<code>path_or_paths</code> is larger than <code>num_proc</code>.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
						<p class="font-medium">Deprecated in 2.6.2</p>
						
<p>Pass <code>DownloadConfig(num_proc=&lt;num_proc&gt;)</code> to the initializer instead.</p>

					</div>`,name:"num_proc"}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/download/download_manager.py#L403",returnDescription:`
<p><code>str</code>, The extracted paths matching the given input
path_or_paths.</p>
`,returnType:`
<p>extracted_path(s)</p>
`}}),at=new T({props:{anchor:"datasets.DownloadManager.extract.example",$$slots:{default:[vg]},$$scope:{ctx:k}}}),Zt=new j({props:{name:"iter_archive",anchor:"datasets.DownloadManager.iter_archive",parameters:[{name:"path_or_buf",val:": typing.Union[str, _io.BufferedReader]"}],parametersDescription:[{anchor:"datasets.DownloadManager.iter_archive.path_or_buf",description:`<strong>path_or_buf</strong> (<code>str</code> or <code>io.BufferedReader</code>) &#x2014;
Archive path or archive binary file object.`,name:"path_or_buf"}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/download/download_manager.py#L359",returnType:`
<p><code>tuple[str, io.BufferedReader]</code></p>
`,isYield:!0}}),st=new T({props:{anchor:"datasets.DownloadManager.iter_archive.example",$$slots:{default:[wg]},$$scope:{ctx:k}}}),ea=new j({props:{name:"iter_files",anchor:"datasets.DownloadManager.iter_files",parameters:[{name:"paths",val:": typing.Union[str, typing.List[str]]"}],parametersDescription:[{anchor:"datasets.DownloadManager.iter_files.paths",description:`<strong>paths</strong> (<code>str</code> or <code>list</code> of <code>str</code>) &#x2014;
Root paths.`,name:"paths"}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/download/download_manager.py#L384",returnType:`
<p><code>str</code></p>
`,isYield:!0}}),ot=new T({props:{anchor:"datasets.DownloadManager.iter_files.example",$$slots:{default:[xg]},$$scope:{ctx:k}}}),ta=new j({props:{name:"ship_files_with_pipeline",anchor:"datasets.DownloadManager.ship_files_with_pipeline",parameters:[{name:"downloaded_path_or_paths",val:""},{name:"pipeline",val:""}],parametersDescription:[{anchor:"datasets.DownloadManager.ship_files_with_pipeline.downloaded_path_or_paths",description:`<strong>downloaded_path_or_paths</strong> (<code>str</code> or <code>list[str]</code> or <code>dict[str, str]</code>) &#x2014;
Nested structure containing the
downloaded path(s).`,name:"downloaded_path_or_paths"},{anchor:"datasets.DownloadManager.ship_files_with_pipeline.pipeline",description:`<strong>pipeline</strong> (<code>utils.beam_utils.BeamPipeline</code>) &#x2014;
Apache Beam Pipeline.`,name:"pipeline"}],source:"https://github.com/huggingface/datasets/blob/2.8.0/src/datasets/download/download_manager.py#L197",returnDescription:`
<p><code>str</code> or <code>list[str]</code> or <code>dict[str, str]</code></p>
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`),Oo=r(hs,"CODE",{});var Om=d(Oo);ic=p(Om,"GeneratorBasedBuilder._split_generators"),Om.forEach(a),cc=p(hs,`.
See `),qo=r(hs,"CODE",{});var qm=d(qo);pc=p(qm,"GeneratorBasedBuilder._split_generators"),qm.forEach(a),mc=p(hs,` for more info and example
of usage.`),hs.forEach(a),gc=g(Tt),v(mt.$$.fragment,Tt),Tt.forEach(a),Lr=g(s),A=r(s,"DIV",{class:!0});var G=d(A);v($a.$$.fragment,G),fc=g(G),Fa=r(G,"P",{});var wp=d(Fa);Mo=r(wp,"CODE",{});var Mm=d(Mo);uc=p(Mm,"Enum"),Mm.forEach(a),_c=p(wp," for dataset splits."),wp.forEach(a),hc=g(G),Vo=r(G,"P",{});var Vm=d(Vo);$c=p(Vm,`Datasets are typically split into different subsets to be used at various
stages of training and evaluation.`),Vm.forEach(a),bc=g(G),oe=r(G,"UL",{});var St=d(oe);Ga=r(St,"LI",{});var xp=d(Ga);Uo=r(xp,"CODE",{});var Um=d(Uo);vc=p(Um,"TRAIN"),Um.forEach(a),wc=p(xp,": the training data."),xp.forEach(a),xc=g(St),Ha=r(St,"LI",{});var Dp=d(Ha);zo=r(Dp,"CODE",{});var zm=d(zo);Dc=p(zm,"VALIDATION"),zm.forEach(a),Ec=p(Dp,`: the validation data. If present, this is typically used as
evaluation data while iterating on a model (e.g. changing hyperparameters,
model architecture, etc.).`),Dp.forEach(a),yc=g(St),Wa=r(St,"LI",{});var Ep=d(Wa);Fo=r(Ep,"CODE",{});var Fm=d(Fo);kc=p(Fm,"TEST"),Fm.forEach(a),jc=p(Ep,`: the testing data. This is the data to report metrics on. Typically
you do not want to use this during model iteration as you may overfit to it.`),Ep.forEach(a),Tc=g(St),Xa=r(St,"LI",{});var yp=d(Xa);Go=r(yp,"CODE",{});var Gm=d(Go);Sc=p(Gm,"ALL"),Gm.forEach(a),Ic=p(yp,": the union of all defined dataset splits."),yp.forEach(a),St.forEach(a),Bc=g(G),ba=r(G,"P",{});var dn=d(ba);Nc=p(dn,"All splits, including compositions inherit from "),Ho=r(dn,"CODE",{});var Hm=d(Ho);Pc=p(Hm,"datasets.SplitBase"),Hm.forEach(a),Cc=p(dn,"."),dn.forEach(a),Rc=g(G),va=r(G,"P",{});var ln=d(va);Lc=p(ln,"See the "),Ka=r(ln,"A",{href:!0});var Wm=d(Ka);Ac=p(Wm,"guide"),Wm.forEach(a),Oc=p(ln," on splits for more information."),ln.forEach(a),qc=g(G),v(gt.$$.fragment,G),G.forEach(a),Ar=g(s),C=r(s,"DIV",{class:!0});var M=d(C);v(wa.$$.fragment,M),Mc=g(M),Wo=r(M,"P",{});var Xm=d(Wo);Vc=p(Xm,"Descriptor corresponding to a named split (train, test, \u2026)."),Xm.forEach(a),Uc=g(M),Xo=r(M,"P",{});var Km=d(Xo);zc=p(Km,"Example:"),Km.forEach(a),Fc=g(M),v(ft.$$.fragment,M),Gc=g(M),Ko=r(M,"P",{});var Jm=d(Ko);Hc=p(Jm,`The resulting split will correspond to 25% of the train split merged with
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