datasets/pr_7889/en/_app/immutable/nodes/14.ef8cd2f4.js

import{s as bt,n as Mt,o as jt}from"../chunks/scheduler.d75c11ed.js";import{S as $t,i as _t,e as o,s as l,c as p,h as kt,a as r,d as s,b as n,f as ie,g as d,j as i,k as Ae,l as f,m as a,n as c,t as m,o as u,p as h}from"../chunks/index.4ec9dfe9.js";import{C as vt,H as Le,E as Ut}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.6dc5a5b5.js";import{C as J}from"../chunks/CodeBlock.c9d7c25c.js";function Gt(Ke){let w,pe,oe,de,$,ce,_,me,k,Oe='Sometimes, you may need to create a dataset if you’re working with your own data. Creating a dataset with 🤗 Datasets confers all the advantages of the library to your dataset: fast loading and processing, <a href="stream">stream enormous datasets</a>, <a href="https://huggingface.co/course/chapter5/4?fw=pt#the-magic-of-memory-mapping" rel="nofollow">memory-mapping</a>, and more. You can easily and rapidly create a dataset with 🤗 Datasets low-code approaches, reducing the time it takes to start training a model. In many cases, it is as easy as <a href="upload_dataset#upload-with-the-hub-ui">dragging and dropping</a> your data files into a dataset repository on the Hub.',ue,v,et="In this tutorial, you’ll learn how to use 🤗 Datasets low-code methods for creating all types of datasets:",he,U,tt="<li>Folder-based builders for quickly creating an image or audio dataset</li> <li><code>from_</code> methods for creating datasets from local files</li>",fe,G,ge,I,st="🤗 Datasets supports many common formats such as <code>csv</code>, <code>json/jsonl</code>, <code>parquet</code>, <code>txt</code>.",ye,q,at="For example it can read a dataset made up of one or several CSV files (in this case, pass your CSV files as a list):",we,Z,Je,C,lt='To get the list of supported formats and code examples, follow this guide <a href="https://huggingface.co/docs/datasets/loading#local-and-remote-files" rel="nofollow">here</a>.',Te,x,be,R,nt="There are two folder-based builders, <code>ImageFolder</code> and <code>AudioFolder</code>. These are low-code methods for quickly creating an image or speech and audio dataset with several thousand examples. They are great for rapidly prototyping computer vision and speech models before scaling to a larger dataset. Folder-based builders takes your data and automatically generates the dataset’s features, splits, and labels. Under the hood:",Me,F,ot='<li><code>ImageFolder</code> uses the <a href="/docs/datasets/pr_7889/en/package_reference/main_classes#datasets.Image">Image</a> feature to decode an image file. Many image extension formats are supported, such as jpg and png, but other formats are also supported. You can check the complete <a href="https://github.com/huggingface/datasets/blob/b5672a956d5de864e6f5550e493527d962d6ae55/src/datasets/packaged_modules/imagefolder/imagefolder.py#L39" rel="nofollow">list</a> of supported image extensions.</li> <li><code>AudioFolder</code> uses the <a href="/docs/datasets/pr_7889/en/package_reference/main_classes#datasets.Audio">Audio</a> feature to decode an audio file. Extensions such as wav, mp3, and even mp4 are supported, and you can check the complete <a href="https://ffmpeg.org/ffmpeg-formats.html" rel="nofollow">list</a> of supported audio extensions. Decoding is done via ffmpeg.</li>',je,X,rt="The dataset splits are generated from the repository structure, and the label names are automatically inferred from the directory name.",$e,B,it="For example, if your image dataset (it is the same for an audio dataset) is stored like this:",_e,W,ke,H,pt="Then this is how the folder-based builder generates an example:",ve,T,dt='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/folder-based-builder.png"/>',Ue,Y,ct='Create the image dataset by specifying <code>imagefolder</code> in <a href="/docs/datasets/pr_7889/en/package_reference/loading_methods#datasets.load_dataset">load_dataset()</a>:',Ge,z,Ie,L,mt='An audio dataset is created in the same way, except you specify <code>audiofolder</code> in <a href="/docs/datasets/pr_7889/en/package_reference/loading_methods#datasets.load_dataset">load_dataset()</a> instead:',qe,N,Ze,Q,ut="Any additional information about your dataset, such as text captions or transcriptions, can be included with a <code>metadata.csv</code> file in the folder containing your dataset. The metadata file needs to have a <code>file_name</code> column that links the image or audio file to its corresponding metadata:",Ce,V,xe,S,ht='To learn more about each of these folder-based builders, check out the and <a href="https://huggingface.co/docs/datasets/image_dataset#imagefolder"><span class="underline decoration-yellow-400 decoration-2 font-semibold">ImageFolder</span></a> or <a href="https://huggingface.co/docs/datasets/audio_dataset#audiofolder"><span class="underline decoration-pink-400 decoration-2 font-semibold">AudioFolder</span></a> guides.',Re,P,Fe,E,ft="You can also create a dataset from data in Python dictionaries. There are two ways you can create a dataset using the <code>from_</code> methods:",Xe,b,g,se,gt='The <a href="/docs/datasets/pr_7889/en/package_reference/main_classes#datasets.Dataset.from_generator">from_generator()</a> method is the most memory-efficient way to create a dataset from a <a href="https://wiki.python.org/moin/Generators" rel="nofollow">generator</a> due to a generators iterative behavior. This is especially useful when you’re working with a really large dataset that may not fit in memory, since the dataset is generated on disk progressively and then memory-mapped.',Ne,D,Qe,ae,yt='A generator-based <a href="/docs/datasets/pr_7889/en/package_reference/main_classes#datasets.IterableDataset">IterableDataset</a> needs to be iterated over with a <code>for</code> loop for example:',Ve,A,Se,y,le,wt='The <a href="/docs/datasets/pr_7889/en/package_reference/main_classes#datasets.Dataset.from_dict">from_dict()</a> method is a straightforward way to create a dataset from a dictionary:',Pe,K,Ee,ne,Jt='To create an image or audio dataset, chain the <a href="/docs/datasets/pr_7889/en/package_reference/main_classes#datasets.Dataset.cast_column">cast_column()</a> method with <a href="/docs/datasets/pr_7889/en/package_reference/main_classes#datasets.Dataset.from_dict">from_dict()</a> and specify the column and feature type. For example, to create an audio dataset:',De,O,Be,ee,Tt="Now that you know how to create a dataset, consider sharing it on the Hub so the community can also benefit from your work! Go on to the next section to learn how to share your dataset.",We,te,He,re,Ye;return $=new vt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),_=new Le({props:{title:"Create a dataset",local:"create-a-dataset",headingTag:"h1"}}),G=new Le({props:{title:"File-based builders",local:"file-based-builders",headingTag:"h2"}}),Z=new J({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJjc3YlMjIlMkMlMjBkYXRhX2ZpbGVzJTNEJTIybXlfZmlsZS5jc3YlMjIp",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">&quot;csv&quot;</span>, data_files=<span class="hljs-string">&quot;my_file.csv&quot;</span>)`,wrap:!1}}),x=new Le({props:{title:"Folder-based builders",local:"folder-based-builders",headingTag:"h2"}}),W=new J({props:{code:"cG9rZW1vbiUyRnRyYWluJTJGZ3Jhc3MlMkZidWxiYXNhdXIucG5nJTBBcG9rZW1vbiUyRnRyYWluJTJGZmlyZSUyRmNoYXJtYW5kZXIucG5nJTBBcG9rZW1vbiUyRnRyYWluJTJGd2F0ZXIlMkZzcXVpcnRsZS5wbmclMEElMEFwb2tlbW9uJTJGdGVzdCUyRmdyYXNzJTJGaXZ5c2F1ci5wbmclMEFwb2tlbW9uJTJGdGVzdCUyRmZpcmUlMkZjaGFybWVsZW9uLnBuZyUwQXBva2Vtb24lMkZ0ZXN0JTJGd2F0ZXIlMkZ3YXJ0b3J0bGUucG5n",highlighted:`pokemon<span class="hljs-regexp">/train/g</span>rass/bulbasaur.png
pokemon<span class="hljs-regexp">/train/</span>fire/charmander.png
pokemon<span class="hljs-regexp">/train/</span>water/squirtle.png

pokemon<span class="hljs-regexp">/test/g</span>rass/ivysaur.png
pokemon<span class="hljs-regexp">/test/</span>fire/charmeleon.png
pokemon<span class="hljs-regexp">/test/</span>water/wartortle.png`,wrap:!1}}),z=new J({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJpbWFnZWZvbGRlciUyMiUyQyUyMGRhdGFfZGlyJTNEJTIyJTJGcGF0aCUyRnRvJTJGcG9rZW1vbiUyMik=",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">&quot;imagefolder&quot;</span>, data_dir=<span class="hljs-string">&quot;/path/to/pokemon&quot;</span>)`,wrap:!1}}),N=new J({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJhdWRpb2ZvbGRlciUyMiUyQyUyMGRhdGFfZGlyJTNEJTIyJTJGcGF0aCUyRnRvJTJGZm9sZGVyJTIyKQ==",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">&quot;audiofolder&quot;</span>, data_dir=<span class="hljs-string">&quot;/path/to/folder&quot;</span>)`,wrap:!1}}),V=new J({props:{code:"ZmlsZV9uYW1lJTJDJTIwdGV4dCUwQWJ1bGJhc2F1ci5wbmclMkMlMjBUaGVyZSUyMGlzJTIwYSUyMHBsYW50JTIwc2VlZCUyMG9uJTIwaXRzJTIwYmFjayUyMHJpZ2h0JTIwZnJvbSUyMHRoZSUyMGRheSUyMHRoaXMlMjBQb2slQzMlQTltb24lMjBpcyUyMGJvcm4uJTBBY2hhcm1hbmRlci5wbmclMkMlMjBJdCUyMGhhcyUyMGElMjBwcmVmZXJlbmNlJTIwZm9yJTIwaG90JTIwdGhpbmdzLiUwQXNxdWlydGxlLnBuZyUyQyUyMFdoZW4lMjBpdCUyMHJldHJhY3RzJTIwaXRzJTIwbG9uZyUyMG5lY2slMjBpbnRvJTIwaXRzJTIwc2hlbGwlMkMlMjBpdCUyMHNxdWlydHMlMjBvdXQlMjB3YXRlciUyMHdpdGglMjB2aWdvcm91cyUyMGZvcmNlLg==",highlighted:`file_name, <span class="hljs-built_in">text</span>
bulbasaur.png, There <span class="hljs-keyword">is</span> a plant seed <span class="hljs-keyword">on</span> <span class="hljs-keyword">its</span> <span class="hljs-keyword">back</span> right <span class="hljs-keyword">from</span> <span class="hljs-keyword">the</span> <span class="hljs-built_in">day</span> this Pokémon <span class="hljs-keyword">is</span> born.
charmander.png, It has a preference <span class="hljs-keyword">for</span> hot things.
squirtle.png, When <span class="hljs-keyword">it</span> retracts <span class="hljs-keyword">its</span> long neck <span class="hljs-keyword">into</span> <span class="hljs-keyword">its</span> shell, <span class="hljs-keyword">it</span> squirts out water <span class="hljs-keyword">with</span> vigorous force.`,wrap:!1}}),P=new Le({props:{title:"From Python dictionaries",local:"from-python-dictionaries",headingTag:"h2"}}),D=new J({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwRGF0YXNldCUwQWRlZiUyMGdlbigpJTNBJTBBJTIwJTIwJTIwJTIweWllbGQlMjAlN0IlMjJwb2tlbW9uJTIyJTNBJTIwJTIyYnVsYmFzYXVyJTIyJTJDJTIwJTIydHlwZSUyMiUzQSUyMCUyMmdyYXNzJTIyJTdEJTBBJTIwJTIwJTIwJTIweWllbGQlMjAlN0IlMjJwb2tlbW9uJTIyJTNBJTIwJTIyc3F1aXJ0bGUlMjIlMkMlMjAlMjJ0eXBlJTIyJTNBJTIwJTIyd2F0ZXIlMjIlN0QlMEFkcyUyMCUzRCUyMERhdGFzZXQuZnJvbV9nZW5lcmF0b3IoZ2VuKSUwQWRzJTVCMCU1RA==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> Dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">gen</span>():
<span class="hljs-meta">... </span>    <span class="hljs-keyword">yield</span> {<span class="hljs-string">&quot;pokemon&quot;</span>: <span class="hljs-string">&quot;bulbasaur&quot;</span>, <span class="hljs-string">&quot;type&quot;</span>: <span class="hljs-string">&quot;grass&quot;</span>}
<span class="hljs-meta">... </span>    <span class="hljs-keyword">yield</span> {<span class="hljs-string">&quot;pokemon&quot;</span>: <span class="hljs-string">&quot;squirtle&quot;</span>, <span class="hljs-string">&quot;type&quot;</span>: <span class="hljs-string">&quot;water&quot;</span>}
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = Dataset.from_generator(gen)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds[<span class="hljs-number">0</span>]
{<span class="hljs-string">&quot;pokemon&quot;</span>: <span class="hljs-string">&quot;bulbasaur&quot;</span>, <span class="hljs-string">&quot;type&quot;</span>: <span class="hljs-string">&quot;grass&quot;</span>}`,wrap:!1}}),A=new J({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwSXRlcmFibGVEYXRhc2V0JTBBZHMlMjAlM0QlMjBJdGVyYWJsZURhdGFzZXQuZnJvbV9nZW5lcmF0b3IoZ2VuKSUwQWZvciUyMGV4YW1wbGUlMjBpbiUyMGRzJTNBJTBBJTIwJTIwJTIwJTIwcHJpbnQoZXhhbXBsZSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> IterableDataset
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = IterableDataset.from_generator(gen)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> example <span class="hljs-keyword">in</span> ds:
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(example)
{<span class="hljs-string">&quot;pokemon&quot;</span>: <span class="hljs-string">&quot;bulbasaur&quot;</span>, <span class="hljs-string">&quot;type&quot;</span>: <span class="hljs-string">&quot;grass&quot;</span>}
{<span class="hljs-string">&quot;pokemon&quot;</span>: <span class="hljs-string">&quot;squirtle&quot;</span>, <span class="hljs-string">&quot;type&quot;</span>: <span class="hljs-string">&quot;water&quot;</span>}`,wrap:!1}}),K=new J({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwRGF0YXNldCUwQWRzJTIwJTNEJTIwRGF0YXNldC5mcm9tX2RpY3QoJTdCJTIycG9rZW1vbiUyMiUzQSUyMCU1QiUyMmJ1bGJhc2F1ciUyMiUyQyUyMCUyMnNxdWlydGxlJTIyJTVEJTJDJTIwJTIydHlwZSUyMiUzQSUyMCU1QiUyMmdyYXNzJTIyJTJDJTIwJTIyd2F0ZXIlMjIlNUQlN0QpJTBBZHMlNUIwJTVE",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> Dataset
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = Dataset.from_dict({<span class="hljs-string">&quot;pokemon&quot;</span>: [<span class="hljs-string">&quot;bulbasaur&quot;</span>, <span class="hljs-string">&quot;squirtle&quot;</span>], <span class="hljs-string">&quot;type&quot;</span>: [<span class="hljs-string">&quot;grass&quot;</span>, <span class="hljs-string">&quot;water&quot;</span>]})
<span class="hljs-meta">&gt;&gt;&gt; </span>ds[<span class="hljs-number">0</span>]
{<span class="hljs-string">&quot;pokemon&quot;</span>: <span class="hljs-string">&quot;bulbasaur&quot;</span>, <span class="hljs-string">&quot;type&quot;</span>: <span class="hljs-string">&quot;grass&quot;</span>}`,wrap:!1}}),O=new J({props:{code:"YXVkaW9fZGF0YXNldCUyMCUzRCUyMERhdGFzZXQuZnJvbV9kaWN0KCU3QiUyMmF1ZGlvJTIyJTNBJTIwJTVCJTIycGF0aCUyRnRvJTJGYXVkaW9fMSUyMiUyQyUyMC4uLiUyQyUyMCUyMnBhdGglMkZ0byUyRmF1ZGlvX24lMjIlNUQlN0QpLmNhc3RfY29sdW1uKCUyMmF1ZGlvJTIyJTJDJTIwQXVkaW8oKSk=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>audio_dataset = Dataset.from_dict({<span class="hljs-string">&quot;audio&quot;</span>: [<span class="hljs-string">&quot;path/to/audio_1&quot;</span>, ..., <span class="hljs-string">&quot;path/to/audio_n&quot;</span>]}).cast_column(<span class="hljs-string">&quot;audio&quot;</span>, Audio())',wrap:!1}}),te=new 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