import{s as ge,n as he,o as ue}from"../chunks/scheduler.12fdeddc.js";import{S as Me,i as ye,g,s as a,r as i,A as ce,h,f as s,c as n,j as re,u as m,x as u,k as At,y as we,a as l,v as d,d as p,t as o,w as r}from"../chunks/index.88dae322.js";import{C as y}from"../chunks/CodeBlock.8e54a086.js";import{H as M,E as be}from"../chunks/getInferenceSnippets.00466244.js";function fe(_t){let c,nt,lt,it,b,mt,f,qt='<a href="https://apple.github.io/embedding-atlas/" rel="nofollow">Embedding Atlas</a> is an interactive visualization tool for exploring large embedding spaces. It enables you to visualize, cross-filter, and search embeddings alongside associated metadata, helping you understand patterns and relationships in high-dimensional data. All computation happens in your computer, ensuring your data remains private and secure.',dt,J,Pt='Here is an <a href="https://huggingface.co/spaces/davanstrien/megascience" rel="nofollow">example atlas</a> for the <a href="https://huggingface.co/datasets/MegaScience/MegaScience" rel="nofollow">MegaScience</a> dataset hosted as a Static Space:',pt,w,Dt='<a href="https://huggingface.co/spaces/davanstrien/megascience"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/atlas-dataset-library-screenshot.png"/></a>',ot,$,rt,T,Kt="<li><strong>Interactive exploration</strong>: Navigate through millions of embeddings with smooth, responsive visualization</li> <li><strong>Browser-based computation</strong>: Compute embeddings and projections locally without sending data to external servers</li> <li><strong>Cross-filtering</strong>: Link and filter data across multiple metadata columns</li> <li><strong>Search capabilities</strong>: Find similar data points to a given query or existing item</li> <li><strong>Multiple integration options</strong>: Use via command line, Jupyter widgets, or web interface</li>",gt,Z,ht,j,Ot="First, install Embedding Atlas:",ut,U,Mt,G,te='If you plan to load private datasets from the Hugging Face Hub, you’ll also need to <a href="/docs/huggingface_hub/quick-start#login">login with your Hugging Face account</a>:',yt,I,ct,W,wt,C,ee="Embedding Atlas provides seamless integration with the Hugging Face Hub, allowing you to visualize embeddings from any dataset directly.",bt,R,ft,k,se="The simplest way to visualize a Hugging Face dataset is through the command line interface. Try it with the IMDB dataset:",Jt,X,$t,B,le="For your own datasets, use the same pattern:",Tt,x,Zt,v,jt,F,ae="You can also use Embedding Atlas in Jupyter notebooks for interactive exploration:",Ut,E,Gt,Y,ne="For your own datasets:",It,H,Wt,V,Ct,Q,ie="If you have datasets with pre-computed embeddings, you can load them directly:",Rt,L,kt,S,Xt,N,me='Embedding Atlas uses <a href="https://huggingface.co/sentence-transformers" rel="nofollow">SentenceTransformers</a> by default but supports custom embedding models:',Bt,z,xt,A,vt,_,de="Fine-tune the dimensionality reduction for your specific use case:",Ft,q,Et,P,Yt,D,Ht,K,pe="Visualize and explore text corpora to identify clusters, outliers, and patterns:",Vt,O,Qt,tt,Lt,et,oe='<li><a href="https://github.com/apple/embedding-atlas" rel="nofollow">Embedding Atlas GitHub Repository</a></li> <li><a href="https://apple.github.io/embedding-atlas/" rel="nofollow">Official Documentation</a></li> <li><a href="https://apple.github.io/embedding-atlas/upload/" rel="nofollow">Interactive Demo</a></li> <li><a href="https://apple.github.io/embedding-atlas/tool.html" rel="nofollow">Command Line Reference</a></li>',St,st,Nt,at,zt;return b=new M({props:{title:"Embedding Atlas",local:"embedding-atlas",headingTag:"h1"}}),$=new M({props:{title:"Key Features",local:"key-features",headingTag:"h2"}}),Z=new M({props:{title:"Prerequisites",local:"prerequisites",headingTag:"h2"}}),U=new y({props:{code:"cGlwJTIwaW5zdGFsbCUyMGVtYmVkZGluZy1hdGxhcw==",highlighted:"pip install embedding-atlas",wrap:!1}}),I=new y({props:{code:"aGYlMjBhdXRoJTIwbG9naW4=",highlighted:"hf auth login",wrap:!1}}),W=new M({props:{title:"Loading Datasets from the Hub",local:"loading-datasets-from-the-hub",headingTag:"h2"}}),R=new M({props:{title:"Using the Command Line",local:"using-the-command-line",headingTag:"h3"}}),X=new y({props:{code:"JTIzJTIwTG9hZCUyMHRoZSUyMElNREIlMjBkYXRhc2V0JTIwZnJvbSUyMHRoZSUyMEh1YiUwQWVtYmVkZGluZy1hdGxhcyUyMHN0YW5mb3JkbmxwJTJGaW1kYiUwQSUwQSUyMyUyMFNwZWNpZnklMjB0aGUlMjB0ZXh0JTIwY29sdW1uJTIwZm9yJTIwZW1iZWRkaW5nJTIwY29tcHV0YXRpb24lMEFlbWJlZGRpbmctYXRsYXMlMjBzdGFuZm9yZG5scCUyRmltZGIlMjAtLXRleHQlMjAlMjJ0ZXh0JTIyJTBBJTBBJTIzJTIwTG9hZCUyMG9ubHklMjBhJTIwc2FtcGxlJTIwZm9yJTIwZmFzdGVyJTIwZXhwbG9yYXRpb24lMEFlbWJlZGRpbmctYXRsYXMlMjBzdGFuZm9yZG5scCUyRmltZGIlMjAtLXRleHQlMjAlMjJ0ZXh0JTIyJTIwLS1zYW1wbGUlMjA1MDAw",highlighted:`<span class="hljs-comment"># Load the IMDB dataset from the Hub</span>
embedding-atlas stanfordnlp/imdb

<span class="hljs-comment"># Specify the text column for embedding computation</span>
embedding-atlas stanfordnlp/imdb --text <span class="hljs-string">&quot;text&quot;</span>

<span class="hljs-comment"># Load only a sample for faster exploration</span>
embedding-atlas stanfordnlp/imdb --text <span class="hljs-string">&quot;text&quot;</span> --sample 5000`,wrap:!1}}),x=new y({props:{code:"JTIzJTIwTG9hZCUyMHlvdXIlMjBkYXRhc2V0JTIwZnJvbSUyMHRoZSUyMEh1YiUwQWVtYmVkZGluZy1hdGxhcyUyMHVzZXJuYW1lJTJGZGF0YXNldC1uYW1lJTBBJTBBJTIzJTIwTG9hZCUyMG11bHRpcGxlJTIwc3BsaXRzJTBBZW1iZWRkaW5nLWF0bGFzJTIwdXNlcm5hbWUlMkZkYXRhc2V0LW5hbWUlMjAtLXNwbGl0JTIwdHJhaW4lMjAtLXNwbGl0JTIwdGVzdCUwQSUwQSUyMyUyMFNwZWNpZnklMjBjdXN0b20lMjB0ZXh0JTIwY29sdW1uJTBBZW1iZWRkaW5nLWF0bGFzJTIwdXNlcm5hbWUlMkZkYXRhc2V0LW5hbWUlMjAtLXRleHQlMjAlMjJjb250ZW50JTIy",highlighted:`<span class="hljs-comment"># Load your dataset from the Hub</span>
embedding-atlas username/dataset-name

<span class="hljs-comment"># Load multiple splits</span>
embedding-atlas username/dataset-name --<span class="hljs-built_in">split</span> train --<span class="hljs-built_in">split</span> <span class="hljs-built_in">test</span>

<span class="hljs-comment"># Specify custom text column</span>
embedding-atlas username/dataset-name --text <span class="hljs-string">&quot;content&quot;</span>`,wrap:!1}}),v=new M({props:{title:"Using Python and Jupyter",local:"using-python-and-jupyter",headingTag:"h3"}}),E=new y({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> embedding_atlas.widget <span class="hljs-keyword">import</span> EmbeddingAtlasWidget
<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd

<span class="hljs-comment"># Load the IMDB dataset from Hugging Face Hub</span>
dataset = load_dataset(<span class="hljs-string">&quot;stanfordnlp/imdb&quot;</span>, split=<span class="hljs-string">&quot;train[:5000]&quot;</span>)

<span class="hljs-comment"># Convert to pandas DataFrame</span>
df = dataset.to_pandas()

<span class="hljs-comment"># Create interactive widget</span>
widget = EmbeddingAtlasWidget(df)
widget`,wrap:!1}}),H=new y({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> embedding_atlas.widget <span class="hljs-keyword">import</span> EmbeddingAtlasWidget
<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd

<span class="hljs-comment"># Load your dataset from the Hub</span>
dataset = load_dataset(<span class="hljs-string">&quot;username/dataset-name&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
df = dataset.to_pandas()

<span class="hljs-comment"># Create interactive widget</span>
widget = EmbeddingAtlasWidget(df)
widget`,wrap:!1}}),V=new M({props:{title:"Working with Pre-computed Embeddings",local:"working-with-pre-computed-embeddings",headingTag:"h3"}}),L=new y({props:{code:"JTIzJTIwTG9hZCUyMGRhdGFzZXQlMjB3aXRoJTIwcHJlLWNvbXB1dGVkJTIwY29vcmRpbmF0ZXMlMEFlbWJlZGRpbmctYXRsYXMlMjB1c2VybmFtZSUyRmRhdGFzZXQtbmFtZSUyMCU1QyUwQSUyMCUyMCUyMCUyMC0teCUyMCUyMmVtYmVkZGluZ194JTIyJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS15JTIwJTIyZW1iZWRkaW5nX3klMjIlMEElMEElMjMlMjBMb2FkJTIwd2l0aCUyMHByZS1jb21wdXRlZCUyMG5lYXJlc3QlMjBuZWlnaGJvcnMlMEFlbWJlZGRpbmctYXRsYXMlMjB1c2VybmFtZSUyRmRhdGFzZXQtbmFtZSUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tbmVpZ2hib3JzJTIwJTIybmVpZ2hib3JzX2NvbHVtbiUyMg==",highlighted:`<span class="hljs-comment"># Load dataset with pre-computed coordinates</span>
embedding-atlas username/dataset-name \\
    --x <span class="hljs-string">&quot;embedding_x&quot;</span> \\
    --y <span class="hljs-string">&quot;embedding_y&quot;</span>

<span class="hljs-comment"># Load with pre-computed nearest neighbors</span>
embedding-atlas username/dataset-name \\
    --neighbors <span class="hljs-string">&quot;neighbors_column&quot;</span>`,wrap:!1}}),S=new M({props:{title:"Customizing Embeddings",local:"customizing-embeddings",headingTag:"h2"}}),z=new y({props:{code:"JTIzJTIwVXNlJTIwYSUyMHNwZWNpZmljJTIwZW1iZWRkaW5nJTIwbW9kZWwlMEFlbWJlZGRpbmctYXRsYXMlMjBzdGFuZm9yZG5scCUyRmltZGIlMjAlNUMlMEElMjAlMjAlMjAlMjAtLXRleHQlMjAlMjJ0ZXh0JTIyJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1tb2RlbCUyMCUyMnNlbnRlbmNlLXRyYW5zZm9ybWVycyUyRmFsbC1NaW5pTE0tTDYtdjIlMjIlMEElMEElMjMlMjBGb3IlMjBtb2RlbHMlMjByZXF1aXJpbmclMjByZW1vdGUlMjBjb2RlJTIwZXhlY3V0aW9uJTBBZW1iZWRkaW5nLWF0bGFzJTIwdXNlcm5hbWUlMkZkYXRhc2V0LW5hbWUlMjAlNUMlMEElMjAlMjAlMjAlMjAtLW1vZGVsJTIwJTIyY3VzdG9tJTJGbW9kZWwlMjIlMjAlNUMlMEElMjAlMjAlMjAlMjAtLXRydXN0LXJlbW90ZS1jb2Rl",highlighted:`<span class="hljs-comment"># Use a specific embedding model</span>
embedding-atlas stanfordnlp/imdb \\
    --text <span class="hljs-string">&quot;text&quot;</span> \\
    --model <span class="hljs-string">&quot;sentence-transformers/all-MiniLM-L6-v2&quot;</span>

<span class="hljs-comment"># For models requiring remote code execution</span>
embedding-atlas username/dataset-name \\
    --model <span class="hljs-string">&quot;custom/model&quot;</span> \\
    --trust-remote-code`,wrap:!1}}),A=new M({props:{title:"UMAP Projection Parameters",local:"umap-projection-parameters",headingTag:"h3"}}),q=new y({props:{code:"ZW1iZWRkaW5nLWF0bGFzJTIwc3RhbmZvcmRubHAlMkZpbWRiJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS10ZXh0JTIwJTIydGV4dCUyMiUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tdW1hcC1uLW5laWdoYm9ycyUyMDMwJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS11bWFwLW1pbi1kaXN0JTIwMC4xJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS11bWFwLW1ldHJpYyUyMCUyMmNvc2luZSUyMg==",highlighted:`embedding-atlas stanfordnlp/imdb \\
    --text <span class="hljs-string">&quot;text&quot;</span> \\
    --umap-n-neighbors 30 \\
    --umap-min-dist 0.1 \\
    --umap-metric <span class="hljs-string">&quot;cosine&quot;</span>`,wrap:!1}}),P=new M({props:{title:"Use Cases",local:"use-cases",headingTag:"h2"}}),D=new M({props:{title:"Exploring Text Datasets",local:"exploring-text-datasets",headingTag:"h3"}}),O=new y({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> embedding_atlas.widget <span class="hljs-keyword">import</span> EmbeddingAtlasWidget
<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd

<span class="hljs-comment"># Load a text classification dataset</span>
dataset = load_dataset(<span class="hljs-string">&quot;stanfordnlp/imdb&quot;</span>, split=<span class="hljs-string">&quot;train[:5000]&quot;</span>)
df = dataset.to_pandas()

<span class="hljs-comment"># Visualize with metadata</span>
widget = EmbeddingAtlasWidget(df)
widget`,wrap:!1}}),tt=new M({props:{title:"Additional Resources",local:"additional-resources",headingTag:"h2"}}),st=new 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