optimum-neuron/pr_1113/en/_app/immutable/nodes/18.397e079e.js

import{s as Ps,n as Os,o as la}from"../chunks/scheduler.56725da7.js";import{S as sa,i as aa,e,s as M,c as i,h as na,a as p,d as a,b as t,f as Ls,g as y,j as T,k as Ds,l as Ma,m as n,n as J,t as j,o as c,p as U}from"../chunks/index.18a26576.js";import{C as ta}from"../chunks/CopyLLMTxtMenu.c5feff19.js";import{C as r}from"../chunks/CodeBlock.6dd2f5ab.js";import{H as w}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.0f5f04c9.js";function ea(ds){let o,bl,hl,Il,h,ul,m,dl,b,Cs="This notebook demonstrates a practical application of semantic embeddings for comparing translations of literary works. We will compare <strong>Alice in Wonderland</strong> in English and French, extracting chapters and using embeddings from a deployed inference endpoint to verify translation quality and alignment.",Cl,I,Bl,u,Bs="Embedding models convert text into fixed-size numerical vectors (embeddings) that capture semantic meaning in a shared vector space. This enables powerful operations like similarity comparisons between texts, regardless of their surface-level differences.",gl,d,gs="<strong>Qwen3-Embedding models</strong> are multilingual embedding models developed by Alibaba’s Qwen team, supporting 100+ languages in a single model. Key advantages:",Gl,C,Gs="<li><strong>Multilingual support</strong>: Texts in different languages are mapped to the same vector space, enabling cross-lingual similarity comparisons</li> <li><strong>Semantic preservation</strong>: Translations with equivalent meaning generate similar embeddings, perfect for translation verification</li> <li><strong>Efficiency</strong>: The <code>Qwen3-Embedding-4B</code> variant offers excellent performance-to-accuracy tradeoff</li>",Al,B,As="This makes <code>Qwen3-Embedding</code> models ideal for translation quality assurance and cross-lingual document matching tasks.",Zl,g,Zs="Note: the multi-lingual capabilities of the <code>Qwen3-Embeddings-0.6B</code> model are not sufficient for this particular use-case.",Wl,G,_l,A,Ws="Translation verification is a critical task in publishing and localization. By leveraging sentence embeddings, we can:",Xl,Z,_s="<li><strong>Automatically match chapters</strong> between two language versions of a book by comparing chapter title embeddings</li> <li><strong>Verify paragraph correspondence</strong> by finding semantically similar paragraphs between source and translated text</li> <li><strong>Quantify translation quality</strong> using cosine similarity scores as a proxy for semantic fidelity</li>",Nl,W,Xs="This approach works regardless of language pair or linguistic differences because embeddings capture semantic meaning in a shared vector space.",fl,_,Rl,X,Ns="We use two EPUB versions of “Alice’s Adventures in Wonderland”:",Ql,N,fs='<li><strong>Original (English)</strong>: <a href="https://www.gutenberg.org/ebooks/11.epub.noimages" rel="nofollow">Project Gutenberg #11</a></li> <li><strong>Translation (French)</strong>: <a href="https://www.gutenberg.org/ebooks/55456.epub.noimages" rel="nofollow">Project Gutenberg #55456</a></li>',El,f,Vl,R,Rs="<li>Deploy an embedding model to Inference Endpoints</li> <li>Download the two EPUB files from Project Gutenberg</li> <li>Extract chapter text and paragraph content from both books</li> <li>Generate embeddings for all chapters and paragraphs</li> <li>Compute similarity matrices to find matching chapters and paragraphs</li> <li>Analyze and visualize the translation correspondence</li>",Yl,Q,kl,E,Qs="We will deploy our embeddings model on Inference Endpoints, a fully managed service that simplifies inference deployment on Trainium/Inferentia devices, using vLLM.",Fl,V,Es='Please refer to <a href="https://huggingface.co/docs/optimum-neuron/guides/vllm_on_ie" rel="nofollow">this guide</a> for the step-by-step instructions to deploy an LLM model on <a href="https://huggingface.co/docs/inference-endpoints/en/index" rel="nofollow">Inference Endpoints</a>.',zl,Y,Vs="This tutorial has been validated using the <code>Qwen/Qwen3-Embedding-4B</code> model deployed on the smallest <code>INF2</code> instance (2 cores - 32 GB device memory).",Sl,k,Ys="Once it has been deployed, please copy your endpoint URL, it will be required in the next steps.",xl,F,vl,z,ks="Install required libraries and import them to set up the environment.",Hl,S,ql,x,$l,v,Kl,H,Ll,q,Fs="Download Alice in Wonderland in both English and French from Project Gutenberg.",Dl,$,Pl,K,Ol,L,zs="Use ebooklib to parse EPUB files and extract chapter structure with paragraph text.",ls,D,ss,P,as,O,Ss="Set up the OpenAI-compatible client and define utility functions for embedding and similarity computation.",ns,ll,Ms,sl,ts,al,xs="Generate embeddings for all chapter titles and build a correspondence table between English and French versions.",es,nl,ps,Ml,is,tl,vs="Deep dive into a specific chapter pair to examine paragraph-level translation quality.",ys,el,Js,pl,js,il,cs,yl,Us,Jl,Ts,jl,ws,cl,Hs='<li><p><strong>Different Models</strong>: Replace <code>Qwen/Qwen3-Embedding-4B</code> with other embedding models like <code>Qwen/Qwen3-Embedding-8B</code> for potentially better results</p></li> <li><p><strong>Other Books</strong>: Download different works from Project Gutenberg and compare different language pairs</p> <ul><li>“Le tour du monde en 80 jours - Jules Verne” - <a href="https://www.gutenberg.org/ebooks/800.epub.noimages" rel="nofollow">original</a> - <a href="https://www.gutenberg.org/ebooks/103.epub.noimages" rel="nofollow">translation</a></li></ul></li> <li><p><strong>Batch Processing</strong>: Scale to compare many chapters automatically with configurable thresholds</p></li> <li><p><strong>Export Results</strong>: Save correspondence data to JSON or CSV for further analysis</p></li> <li><p><strong>Task Tuning</strong>: Adjust the instruction prompt in <code>embed_texts()</code> to optimize embeddings for your specific use case</p></li>',rs,Ul,os,Tl,qs="<li>Embedding generation time scales with the number of texts and text length</li> <li>Similarity computation is fast (matrix multiplication) once embeddings are available</li> <li>For production use, consider caching embeddings to avoid re-computation</li> <li>Batch size in <code>client.embeddings.create()</code> may need adjustment based on endpoint limits</li>",hs,wl,ms,rl,$s="If embeddings fail to generate:",bs,ol,Ks="<li>Verify endpoint is running and accessible</li> <li>Check your Hugging Face token configuration</li> <li>Review endpoint logs for error messages</li>",Is,ml,us;return h=new ta({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),m=new w({props:{title:"Comparing Book Translations using Embeddings",local:"comparing-book-translations-using-embeddings",headingTag:"h1"}}),I=new w({props:{title:"Background: Embedding Models",local:"background-embedding-models",headingTag:"h3"}}),G=new w({props:{title:"What This Example Illustrates",local:"what-this-example-illustrates",headingTag:"h3"}}),_=new w({props:{title:"Dataset",local:"dataset",headingTag:"h3"}}),f=new w({props:{title:"Workflow",local:"workflow",headingTag:"h3"}}),Q=new w({props:{title:"1. Configure Inference Endpoint",local:"1-configure-inference-endpoint",headingTag:"h2"}}),F=new w({props:{title:"2. Setup and Dependencies",local:"2-setup-and-dependencies",headingTag:"h2"}}),S=new r({props:{code:"JTI1cGlwJTIwaW5zdGFsbCUyMC1xJTIwcmVxdWVzdHMlMjBvcGVuYWklMjB0b3JjaCUyMGVib29rbGliJTIwYnM0JTIwaHVnZ2luZ2ZhY2VfaHViJTIwbWF0cGxvdGxpYiUyMG51bXB5",highlighted:"%pip install -q requests openai torch ebooklib bs4 huggingface_hub matplotlib numpy",lang:"python",wrap:!1}}),x=new r({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> os
<span class="hljs-keyword">import</span> tempfile
<span class="hljs-keyword">from</span> pathlib <span class="hljs-keyword">import</span> Path
<span class="hljs-keyword">from</span> typing <span class="hljs-keyword">import</span> <span class="hljs-type">Dict</span>, <span class="hljs-type">List</span>, <span class="hljs-type">Optional</span>

<span class="hljs-keyword">import</span> ebooklib
<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> torch.nn.functional <span class="hljs-keyword">as</span> F
<span class="hljs-keyword">from</span> bs4 <span class="hljs-keyword">import</span> BeautifulSoup
<span class="hljs-keyword">from</span> ebooklib <span class="hljs-keyword">import</span> epub
<span class="hljs-keyword">from</span> openai <span class="hljs-keyword">import</span> OpenAI
<span class="hljs-keyword">from</span> torch <span class="hljs-keyword">import</span> Tensor
<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> get_token

<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;✓ All dependencies imported successfully&quot;</span>)`,lang:"python",wrap:!1}}),v=new r({props:{code:"JTIzJTIwSW5mZXJlbmNlJTIwRW5kcG9pbnQlMjBDb25maWd1cmF0aW9uJTBBJTBBQkFTRV9VUkwlMjAlM0QlMjBvcy5lbnZpcm9uLmdldCglMjJJTkZFUkVOQ0VfRU5EUE9JTlRfVVJMJTIyKSUwQWlmJTIwbm90JTIwQkFTRV9VUkwlM0ElMEElMjAlMjAlMjAlMjBCQVNFX1VSTCUyMCUzRCUyMGlucHV0KCUyMkVudGVyJTIwdGhlJTIwSW5mZXJlbmNlJTIwRW5kcG9pbnQlMjBVUkwlM0ElMjAlMjIpJTBBVE9LRU4lMjAlM0QlMjBnZXRfdG9rZW4oKSUwQWlmJTIwVE9LRU4lMjBpcyUyME5vbmUlM0ElMEElMjAlMjAlMjAlMjBUT0tFTiUyMCUzRCUyMGlucHV0KCUyMkVudGVyJTIweW91ciUyMEh1Z2dpbmclMjBGYWNlJTIwQVBJJTIwVG9rZW4lM0ElMjAlMjIp",highlighted:`<span class="hljs-comment"># Inference Endpoint Configuration</span>

BASE_URL = os.environ.get(<span class="hljs-string">&quot;INFERENCE_ENDPOINT_URL&quot;</span>)
<span class="hljs-keyword">if</span> <span class="hljs-keyword">not</span> BASE_URL:
    BASE_URL = <span class="hljs-built_in">input</span>(<span class="hljs-string">&quot;Enter the Inference Endpoint URL: &quot;</span>)
TOKEN = get_token()
<span class="hljs-keyword">if</span> TOKEN <span class="hljs-keyword">is</span> <span class="hljs-literal">None</span>:
    TOKEN = <span class="hljs-built_in">input</span>(<span class="hljs-string">&quot;Enter your Hugging Face API Token: &quot;</span>)`,lang:"python",wrap:!1}}),H=new w({props:{title:"3. Download EPUB Files",local:"3-download-epub-files",headingTag:"h2"}}),$=new r({props:{code:"JTIzJTIwRG93bmxvYWQlMjBBbGljZSUyMGluJTIwV29uZGVybGFuZCUyMGluJTIwRW5nbGlzaCUyMGFuZCUyMEZyZW5jaCUwQWVwdWJfZGlyJTIwJTNEJTIwUGF0aCh0ZW1wZmlsZS5ta2R0ZW1wKHByZWZpeCUzRCUyMmFsaWNlXyUyMikpJTBBJTBBVVJMcyUyMCUzRCUyMCU3QiUwQSUyMCUyMCUyMCUyMCUyMm9yaWdpbmFsJTIyJTNBJTIwJTIyaHR0cHMlM0ElMkYlMkZ3d3cuZ3V0ZW5iZXJnLm9yZyUyRmVib29rcyUyRjExLmVwdWIubm9pbWFnZXMlMjIlMkMlMEElMjAlMjAlMjAlMjAlMjJ0cmFuc2xhdGlvbiUyMiUzQSUyMCUyMmh0dHBzJTNBJTJGJTJGd3d3Lmd1dGVuYmVyZy5vcmclMkZlYm9va3MlMkY1NTQ1Ni5lcHViLm5vaW1hZ2VzJTIyJTJDJTBBJTdEJTBBJTBBZXB1Yl9maWxlcyUyMCUzRCUyMCU3QiU3RCUwQWZvciUyMGxhbmclMkMlMjB1cmwlMjBpbiUyMFVSTHMuaXRlbXMoKSUzQSUwQSUyMCUyMCUyMCUyMGZpbGVwYXRoJTIwJTNEJTIwZXB1Yl9kaXIlMjAlMkYlMjBmJTIyYWxpY2VfJTdCbGFuZy5sb3dlcigpJTdELmVwdWIlMjIlMEElMjAlMjAlMjAlMjByZXNwb25zZSUyMCUzRCUyMHJlcXVlc3RzLmdldCh1cmwlMkMlMjB0aW1lb3V0JTNEMTIwKSUwQSUyMCUyMCUyMCUyMHJlc3BvbnNlLnJhaXNlX2Zvcl9zdGF0dXMoKSUwQSUyMCUyMCUyMCUyMGZpbGVwYXRoLndyaXRlX2J5dGVzKHJlc3BvbnNlLmNvbnRlbnQpJTBBJTIwJTIwJTIwJTIwZXB1Yl9maWxlcyU1QmxhbmclNUQlMjAlM0QlMjBmaWxlcGF0aCUwQSUwQXByaW50KGYlMjIlRTIlOUMlOTMlMjBEb3dubG9hZGVkJTIwJTdCbGVuKGVwdWJfZmlsZXMpJTdEJTIwRVBVQiUyMGZpbGVzJTIwdG8lMjAlN0JlcHViX2RpciU3RCUyMik=",highlighted:`<span class="hljs-comment"># Download Alice in Wonderland in English and French</span>
epub_dir = Path(tempfile.mkdtemp(prefix=<span class="hljs-string">&quot;alice_&quot;</span>))

URLs = {
    <span class="hljs-string">&quot;original&quot;</span>: <span class="hljs-string">&quot;https://www.gutenberg.org/ebooks/11.epub.noimages&quot;</span>,
    <span class="hljs-string">&quot;translation&quot;</span>: <span class="hljs-string">&quot;https://www.gutenberg.org/ebooks/55456.epub.noimages&quot;</span>,
}

epub_files = {}
<span class="hljs-keyword">for</span> lang, url <span class="hljs-keyword">in</span> URLs.items():
    filepath = epub_dir / <span class="hljs-string">f&quot;alice_<span class="hljs-subst">{lang.lower()}</span>.epub&quot;</span>
    response = requests.get(url, timeout=<span class="hljs-number">120</span>)
    response.raise_for_status()
    filepath.write_bytes(response.content)
    epub_files[lang] = filepath

<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;✓ Downloaded <span class="hljs-subst">{<span class="hljs-built_in">len</span>(epub_files)}</span> EPUB files to <span class="hljs-subst">{epub_dir}</span>&quot;</span>)`,lang:"python",wrap:!1}}),K=new w({props:{title:"4. Extract Chapters from EPUBs",local:"4-extract-chapters-from-epubs",headingTag:"h2"}}),D=new r({props:{code:"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",highlighted:`<span class="hljs-keyword">def</span> <span class="hljs-title function_">extract_chapters</span>(<span class="hljs-params">book: epub.EpubBook</span>) -&gt; <span class="hljs-type">Dict</span>[<span class="hljs-built_in">str</span>, <span class="hljs-type">List</span>[<span class="hljs-built_in">str</span>]]:
    <span class="hljs-string">&quot;&quot;&quot;Extract chapters and paragraphs from EPUB.&quot;&quot;&quot;</span>

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">flatten_toc_titles</span>(<span class="hljs-params">node, titles: <span class="hljs-type">List</span>[<span class="hljs-built_in">str</span>]</span>) -&gt; <span class="hljs-literal">None</span>:
        <span class="hljs-keyword">if</span> <span class="hljs-built_in">isinstance</span>(node, (<span class="hljs-built_in">list</span>, <span class="hljs-built_in">tuple</span>)):
            <span class="hljs-keyword">for</span> item <span class="hljs-keyword">in</span> node:
                flatten_toc_titles(item, titles)
            <span class="hljs-keyword">return</span>

        title = <span class="hljs-built_in">getattr</span>(node, <span class="hljs-string">&quot;title&quot;</span>, <span class="hljs-literal">None</span>)
        <span class="hljs-keyword">if</span> title:
            titles.append(title)

        subitems = <span class="hljs-built_in">getattr</span>(node, <span class="hljs-string">&quot;subitems&quot;</span>, <span class="hljs-literal">None</span>)
        <span class="hljs-keyword">if</span> subitems:
            flatten_toc_titles(subitems, titles)

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">normalize_title</span>(<span class="hljs-params">text: <span class="hljs-built_in">str</span></span>) -&gt; <span class="hljs-built_in">str</span>:
        <span class="hljs-keyword">return</span> <span class="hljs-string">&quot; &quot;</span>.join(text.replace(<span class="hljs-string">&quot;\\n&quot;</span>, <span class="hljs-string">&quot; &quot;</span>).split()).strip().casefold()

    chapters = {}
    toc_titles: <span class="hljs-type">List</span>[<span class="hljs-built_in">str</span>] = []
    flatten_toc_titles(book.toc, toc_titles)
    toc_title_set = {normalize_title(title) <span class="hljs-keyword">for</span> title <span class="hljs-keyword">in</span> toc_titles}
    documents = <span class="hljs-built_in">list</span>(book.get_items_of_type(ebooklib.ITEM_DOCUMENT))

    current_title: <span class="hljs-type">Optional</span>[<span class="hljs-built_in">str</span>] = <span class="hljs-literal">None</span>
    current_chapter: <span class="hljs-type">List</span>[<span class="hljs-built_in">str</span>] = []

    <span class="hljs-keyword">for</span> doc <span class="hljs-keyword">in</span> documents:
        soup = BeautifulSoup(doc.get_body_content(), <span class="hljs-string">&quot;html.parser&quot;</span>)
        <span class="hljs-keyword">for</span> node <span class="hljs-keyword">in</span> soup.find_all([<span class="hljs-string">&quot;h2&quot;</span>, <span class="hljs-string">&quot;p&quot;</span>]):
            <span class="hljs-keyword">if</span> node.name == <span class="hljs-string">&quot;h2&quot;</span>:
                heading = node.get_text(<span class="hljs-string">&quot; &quot;</span>, strip=<span class="hljs-literal">True</span>)
                normalized_heading = normalize_title(heading)
                <span class="hljs-keyword">if</span> normalized_heading <span class="hljs-keyword">in</span> toc_title_set:
                    <span class="hljs-keyword">if</span> <span class="hljs-built_in">len</span>(current_chapter) &gt; <span class="hljs-number">0</span> <span class="hljs-keyword">and</span> current_title:
                        chapters[current_title] = current_chapter
                    current_title = heading
                    current_chapter = []
                <span class="hljs-keyword">continue</span>

            <span class="hljs-keyword">if</span> node.name == <span class="hljs-string">&quot;p&quot;</span> <span class="hljs-keyword">and</span> current_title:
                text = node.get_text(<span class="hljs-string">&quot; &quot;</span>, strip=<span class="hljs-literal">True</span>)
                <span class="hljs-keyword">if</span> text <span class="hljs-keyword">and</span> <span class="hljs-built_in">any</span>(c.isalnum() <span class="hljs-keyword">for</span> c <span class="hljs-keyword">in</span> text):
                    current_chapter.append(text)

    <span class="hljs-keyword">if</span> current_title <span class="hljs-keyword">and</span> <span class="hljs-built_in">len</span>(current_chapter) &gt; <span class="hljs-number">0</span>:
        chapters[current_title] = current_chapter

    <span class="hljs-keyword">return</span> chapters


<span class="hljs-keyword">def</span> <span class="hljs-title function_">extract_language</span>(<span class="hljs-params">book: epub.EpubBook</span>) -&gt; <span class="hljs-built_in">str</span>:
    <span class="hljs-string">&quot;&quot;&quot;Extract language code from EPUB metadata.&quot;&quot;&quot;</span>
    language_metadata = book.get_metadata(<span class="hljs-string">&quot;DC&quot;</span>, <span class="hljs-string">&quot;language&quot;</span>)
    <span class="hljs-keyword">if</span> language_metadata <span class="hljs-keyword">and</span> <span class="hljs-built_in">len</span>(language_metadata) &gt; <span class="hljs-number">0</span>:
        <span class="hljs-keyword">return</span> language_metadata[<span class="hljs-number">0</span>][<span class="hljs-number">0</span>]  <span class="hljs-comment"># Return language code (e.g., &#x27;en&#x27;, &#x27;fr&#x27;)</span>
    <span class="hljs-keyword">return</span> <span class="hljs-string">&quot;unknown&quot;</span>


<span class="hljs-comment"># Extract chapters from both EPUB files</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Extracting chapters and language metadata...\\n&quot;</span>)
chapters_data = {}
language_map = {}  <span class="hljs-comment"># Map from user-provided key to actual language code</span>

<span class="hljs-keyword">for</span> key, epub_path <span class="hljs-keyword">in</span> epub_files.items():
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Processing <span class="hljs-subst">{key}</span> ...&quot;</span>)
    <span class="hljs-keyword">try</span>:
        book = epub.read_epub(<span class="hljs-built_in">str</span>(epub_path))

        <span class="hljs-comment"># Extract language from metadata</span>
        detected_lang = extract_language(book)
        language_map[key] = detected_lang

        <span class="hljs-comment"># Extract chapters</span>
        chapters = extract_chapters(book)
        chapters_data[key] = chapters

        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  ✓ Found <span class="hljs-subst">{<span class="hljs-built_in">len</span>(chapters)}</span> chapters&quot;</span>)
        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  ✓ Detected language: <span class="hljs-subst">{detected_lang}</span>&quot;</span>)
        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;    Chapters: <span class="hljs-subst">{<span class="hljs-string">&#x27;, &#x27;</span>.join(<span class="hljs-built_in">list</span>(chapters.keys())[:<span class="hljs-number">3</span>])}</span>...&quot;</span>)
    <span class="hljs-keyword">except</span> Exception <span class="hljs-keyword">as</span> e:
        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  ✗ Error: <span class="hljs-subst">{e}</span>&quot;</span>)`,lang:"python",wrap:!1}}),P=new w({props:{title:"5. Initialize Embedding Client and Helper Functions",local:"5-initialize-embedding-client-and-helper-functions",headingTag:"h2"}}),ll=new 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RCUyMHdfdGl0bGUlMjAqJTIwdGl0bGVfc2NvcmUlMjAlMkIlMjB3X2luZGV4JTIwKiUyMGluZGV4X3Njb3JlJTIwJTJCJTIwd19wYXJhcyUyMColMjBwYXJhX3Njb3JlJTBBJTBBJTIwJTIwJTIwJTIwcmV0dXJuJTIwY29tYmluZWRfc2NvcmUlMEElMEElMEFwcmludCglMjIlRTIlOUMlOTMlMjBFbWJlZGRpbmclMjB1dGlsaXRpZXMlMjBpbml0aWFsaXplZCUyMik=",highlighted:`<span class="hljs-comment"># Initialize OpenAI client for embeddings</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Initializing embedding client...&quot;</span>)
client = OpenAI(base_url=BASE_URL + <span class="hljs-string">&quot;/v1&quot;</span>, api_key=TOKEN)

<span class="hljs-comment"># Test connection</span>
<span class="hljs-keyword">try</span>:
    models = client.models.<span class="hljs-built_in">list</span>()
    available_models = [m.<span class="hljs-built_in">id</span> <span class="hljs-keyword">for</span> m <span class="hljs-keyword">in</span> models.data]
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;✓ Connected to embedding service&quot;</span>)
    MODEL_NAME = available_models[<span class="hljs-number">0</span>]
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  Selected model: <span class="hljs-subst">{MODEL_NAME}</span>&quot;</span>)
<span class="hljs-keyword">except</span> Exception <span class="hljs-keyword">as</span> e:
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;✗ Failed to connect: <span class="hljs-subst">{e}</span>&quot;</span>)
    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Make sure your inference endpoint is running&quot;</span>)


<span class="hljs-keyword">def</span> <span class="hljs-title function_">embed_translations</span>(<span class="hljs-params">client: OpenAI, texts: <span class="hljs-type">List</span>[<span class="hljs-built_in">str</span>], model: <span class="hljs-built_in">str</span></span>) -&gt; Tensor:
    <span class="hljs-string">&quot;&quot;&quot;Generate embeddings for a list of texts using the inference endpoint.&quot;&quot;&quot;</span>
    task = <span class="hljs-string">&quot;Given a text, retrieve its translation from the provided documents.&quot;</span>
    instructed_texts = [<span class="hljs-string">f&quot;Instruct: <span class="hljs-subst">{task}</span>\\nQuery: <span class="hljs-subst">{text}</span>&quot;</span> <span class="hljs-keyword">for</span> text <span class="hljs-keyword">in</span> texts]

    resp = client.embeddings.create(<span class="hljs-built_in">input</span>=instructed_texts, model=model)
    embeddings = [torch.tensor(d.embedding, dtype=torch.float32) <span class="hljs-keyword">for</span> d <span class="hljs-keyword">in</span> resp.data]
    embeddings = torch.stack(embeddings)
    embeddings = F.normalize(embeddings, p=<span class="hljs-number">2</span>, dim=<span class="hljs-number">1</span>)
    <span class="hljs-keyword">return</span> embeddings


<span class="hljs-keyword">def</span> <span class="hljs-title function_">similarity_matrix</span>(<span class="hljs-params">emb_a: Tensor, emb_b: Tensor</span>) -&gt; Tensor:
    <span class="hljs-string">&quot;&quot;&quot;Compute cosine similarity matrix between two sets of embeddings.&quot;&quot;&quot;</span>
    <span class="hljs-keyword">return</span> emb_a @ emb_b.T


<span class="hljs-keyword">def</span> <span class="hljs-title function_">find_best_match</span>(<span class="hljs-params">scores: Tensor, threshold: <span class="hljs-built_in">float</span> = <span class="hljs-number">0.0</span></span>) -&gt; <span class="hljs-built_in">int</span>:
    <span class="hljs-string">&quot;&quot;&quot;Find index of best match in similarity scores.&quot;&quot;&quot;</span>
    <span class="hljs-keyword">return</span> torch.argmax(scores).item()


<span class="hljs-keyword">def</span> <span class="hljs-title function_">compute_chapter_match_score</span>(<span class="hljs-params">
    title_sim: <span class="hljs-built_in">float</span>,
    idx_a: <span class="hljs-built_in">int</span>,
    idx_b: <span class="hljs-built_in">int</span>,
    para_count_a: <span class="hljs-built_in">int</span>,
    para_count_b: <span class="hljs-built_in">int</span>,
    max_idx: <span class="hljs-built_in">int</span>,
    w_title: <span class="hljs-built_in">float</span> = <span class="hljs-number">0.7</span>,
    w_index: <span class="hljs-built_in">float</span> = <span class="hljs-number">0.15</span>,
    w_paras: <span class="hljs-built_in">float</span> = <span class="hljs-number">0.15</span>,
</span>) -&gt; <span class="hljs-built_in">float</span>:
    <span class="hljs-string">&quot;&quot;&quot;
    Compute a composite score for chapter matching combining:
    - Title similarity (semantic): weight 0.7
    - Chapter index proximity: weight 0.15
    - Paragraph count similarity: weight 0.15

    This helps identify chapters where title translation is poor but other signals match.
    &quot;&quot;&quot;</span>
    <span class="hljs-comment"># Title similarity (already 0-1 range)</span>
    title_score = title_sim

    <span class="hljs-comment"># Index proximity: 1 - normalized distance</span>
    index_distance = <span class="hljs-built_in">abs</span>(idx_a - idx_b) / <span class="hljs-built_in">max</span>(max_idx, <span class="hljs-number">1</span>)
    index_score = <span class="hljs-number">1.0</span> - index_distance

    <span class="hljs-comment"># Paragraph count similarity: normalized overlap ratio</span>
    max_paras = <span class="hljs-built_in">max</span>(para_count_a, para_count_b)
    min_paras = <span class="hljs-built_in">min</span>(para_count_a, para_count_b)
    para_score = min_paras / <span class="hljs-built_in">max</span>(max_paras, <span class="hljs-number">1</span>)

    <span class="hljs-comment"># Weighted combination</span>
    combined_score = w_title * title_score + w_index * index_score + w_paras * para_score

    <span class="hljs-keyword">return</span> combined_score


<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;✓ Embedding utilities initialized&quot;</span>)`,lang:"python",wrap:!1}}),sl=new w({props:{title:"6. Embed and Compare Chapters",local:"6-embed-and-compare-chapters",headingTag:"h2"}}),nl=new r({props:{code:"print(%22Computing%20chapter%20title%20embeddings%20and%20building%20correspondence...%5Cn%22)%0A%0A%23%20Extract%20chapter%20titles%20from%20both%20versions%0Alangs%20%3D%20list(chapters_data.keys())%0A%0Aoriginal_lang%2C%20translation_lang%20%3D%20langs%5B0%5D%2C%20langs%5B1%5D%0A%0Atitles_original%20%3D%20list(chapters_data%5Boriginal_lang%5D.keys())%0Atitles_translation%20%3D%20list(chapters_data%5Btranslation_lang%5D.keys())%0A%0Aprint(f%22%7Boriginal_lang%7D%3A%20%7Blen(titles_original)%7D%20chapters%22)%0Aprint(f%22%7Btranslation_lang%7D%3A%20%7Blen(titles_translation)%7D%20chapters%5Cn%22)%0A%0A%23%20Embed%20chapter%20titles%0Aprint(f%22Embedding%20%7Blen(titles_original)%20%2B%20len(titles_translation)%7D%20chapter%20titles...%22)%0Atry%3A%0A%20%20%20%20emb_titles_original%20%3D%20embed_translations(client%2C%20titles_original%2C%20MODEL_NAME)%0A%20%20%20%20emb_titles_translation%20%3D%20embed_translations(client%2C%20titles_translation%2C%20MODEL_NAME)%0A%20%20%20%20print(f%22%E2%9C%93%20Embeddings%20complete%5Cn%22)%0A%0A%20%20%20%20%23%20Get%20paragraph%20counts%20for%20each%20chapter%0A%20%20%20%20para_counts_original%20%3D%20%5Blen(chapters_data%5Boriginal_lang%5D%5Btitle%5D)%20for%20title%20in%20titles_original%5D%0A%20%20%20%20para_counts_translation%20%3D%20%5Blen(chapters_data%5Btranslation_lang%5D%5Btitle%5D)%20for%20title%20in%20titles_translation%5D%0A%0A%20%20%20%20%23%20Build%20correspondence%20table%20using%20composite%20scoring%0A%20%20%20%20print(f%22Building%20chapter%20correspondence%20with%20composite%20scoring...%5Cn%22)%0A%20%20%20%20title_scores%20%3D%20similarity_matrix(emb_titles_original%2C%20emb_titles_translation)%0A%0A%20%20%20%20correspondence%20%3D%20%7B%7D%0A%20%20%20%20match_details%20%3D%20%7B%7D%20%20%23%20Store%20detailed%20scoring%20info%0A%0A%20%20%20%20for%20i%2C%20title_original%20in%20enumerate(titles_original)%3A%0A%20%20%20%20%20%20%20%20best_idx%20%3D%20None%0A%20%20%20%20%20%20%20%20best_composite_score%20%3D%20-1%0A%0A%20%20%20%20%20%20%20%20%23%20Check%20all%20candidates%20and%20compute%20composite%20scores%0A%20%20%20%20%20%20%20%20for%20j%20in%20range(len(titles_translation))%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20title_sim%20%3D%20title_scores%5Bi%2C%20j%5D.item()%0A%20%20%20%20%20%20%20%20%20%20%20%20composite_score%20%3D%20compute_chapter_match_score(%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20title_sim%3Dtitle_sim%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20idx_a%3Di%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20idx_b%3Dj%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20para_count_a%3Dpara_counts_original%5Bi%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20para_count_b%3Dpara_counts_translation%5Bj%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20max_idx%3Dmax(len(titles_original)%2C%20len(titles_translation))%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20)%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20composite_score%20%3E%20best_composite_score%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20best_composite_score%20%3D%20composite_score%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20best_idx%20%3D%20j%0A%0A%20%20%20%20%20%20%20%20title_translation%20%3D%20titles_translation%5Bbest_idx%5D%0A%20%20%20%20%20%20%20%20title_sim%20%3D%20title_scores%5Bi%2C%20best_idx%5D.item()%0A%0A%20%20%20%20%20%20%20%20correspondence%5Btitle_original%5D%20%3D%20(title_translation%2C%20best_composite_score)%0A%20%20%20%20%20%20%20%20match_details%5Btitle_original%5D%20%3D%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22title_sim%22%3A%20title_sim%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22composite_score%22%3A%20best_composite_score%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22para_count_a%22%3A%20para_counts_original%5Bi%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22para_count_b%22%3A%20para_counts_translation%5Bbest_idx%5D%2C%0A%20%20%20%20%20%20%20%20%7D%0A%0A%20%20%20%20%23%20Display%20correspondence%20table%0A%20%20%20%20print(%22%2B%22%20%2B%20%22%3D%22%20*%20100%20%2B%20%22%2B%22)%0A%20%20%20%20print(f%22%7C%20%7B'Original'%3A%3C30%7D%20%7C%20%7B'Translation'%3A%3C30%7D%20%7C%20Title%20Sim%20%7C%20Composite%20%7C%22)%0A%20%20%20%20print(%22%2B%22%20%2B%20%22%3D%22%20*%20100%20%2B%20%22%2B%22)%0A%0A%20%20%20%20for%20title_original%2C%20(title_translation%2C%20comp_score)%20in%20correspondence.items()%3A%0A%20%20%20%20%20%20%20%20a_short%20%3D%20title_original%5B%3A27%5D%20if%20len(title_original)%20%3E%2027%20else%20title_original%0A%20%20%20%20%20%20%20%20b_short%20%3D%20title_translation%5B%3A27%5D%20if%20len(title_translation)%20%3E%2027%20else%20title_translation%0A%20%20%20%20%20%20%20%20details%20%3D%20match_details%5Btitle_original%5D%0A%20%20%20%20%20%20%20%20print(f%22%7C%20%7Ba_short%3A%3C30%7D%20%7C%20%7Bb_short%3A%3C30%7D%20%7C%20%7Bdetails%5B'title_sim'%5D%3A%3E8.3f%7D%20%7C%20%7Bcomp_score%3A%3E8.3f%7D%20%7C%22)%0A%0A%20%20%20%20print(%22%2B%22%20%2B%20%22%3D%22%20*%20100%20%2B%20%22%2B%22)%0A%0A%20%20%20%20%23%20Identify%20chapters%20with%20low%20title%20sim%20but%20decent%20composite%20score%20(potential%20translation%20issues)%0A%20%20%20%20print(f%22%5CnChapters%20with%20potential%20translation%20discrepancies%20(title_sim%20%3C%3C%20composite_score)%3A%22)%0A%20%20%20%20discrepancies%20%3D%20%5B%5D%0A%20%20%20%20for%20title_original%2C%20details%20in%20match_details.items()%3A%0A%20%20%20%20%20%20%20%20gap%20%3D%20details%5B%22composite_score%22%5D%20-%20details%5B%22title_sim%22%5D%0A%20%20%20%20%20%20%20%20if%20gap%20%3E%200.15%3A%20%20%23%20Significant%20gap%20between%20scores%0A%20%20%20%20%20%20%20%20%20%20%20%20discrepancies.append((title_original%2C%20gap%2C%20details))%0A%0A%20%20%20%20if%20discrepancies%3A%0A%20%20%20%20%20%20%20%20discrepancies.sort(key%3Dlambda%20x%3A%20x%5B1%5D%2C%20reverse%3DTrue)%0A%20%20%20%20%20%20%20%20for%20title_original%2C%20gap%2C%20details%20in%20discrepancies%5B%3A5%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20print(%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20f%22%20%20-%20%7Btitle_original%5B%3A40%5D%7D%3A%20title_sim%3D%7Bdetails%5B'title_sim'%5D%3A.3f%7D%2C%20composite%3D%7Bdetails%5B'composite_score'%5D%3A.3f%7D%20(gap%3A%20%7Bgap%3A.3f%7D)%22%0A%20%20%20%20%20%20%20%20%20%20%20%20)%0A%20%20%20%20%20%20%20%20%20%20%20%20print(f%22%20%20%20%20Para%20counts%3A%20%7Bdetails%5B'para_count_a'%5D%7D%20vs%20%7Bdetails%5B'para_count_b'%5D%7D%22)%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20print(%22%20%20None%20detected%20-%20title%20translations%20appear%20accurate%22)%0A%0Aexcept%20Exception%20as%20e%3A%0A%20%20%20%20print(f%22Error%20during%20chapter%20embedding%3A%20%7Be%7D%22)%0A%20%20%20%20print(%22Make%20sure%20your%20inference%20endpoint%20is%20running%20and%20accessible%22)",highlighted:`<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Computing chapter title embeddings and building correspondence...\\n&quot;</span>)

<span class="hljs-comment"># Extract chapter titles from both versions</span>
langs = <span class="hljs-built_in">list</span>(chapters_data.keys())

original_lang, translation_lang = langs[<span class="hljs-number">0</span>], langs[<span class="hljs-number">1</span>]

titles_original = <span class="hljs-built_in">list</span>(chapters_data[original_lang].keys())
titles_translation = <span class="hljs-built_in">list</span>(chapters_data[translation_lang].keys())

<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;<span class="hljs-subst">{original_lang}</span>: <span class="hljs-subst">{<span class="hljs-built_in">len</span>(titles_original)}</span> chapters&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;<span class="hljs-subst">{translation_lang}</span>: <span class="hljs-subst">{<span class="hljs-built_in">len</span>(titles_translation)}</span> chapters\\n&quot;</span>)

<span class="hljs-comment"># Embed chapter titles</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Embedding <span class="hljs-subst">{<span class="hljs-built_in">len</span>(titles_original) + <span class="hljs-built_in">len</span>(titles_translation)}</span> chapter titles...&quot;</span>)
<span class="hljs-keyword">try</span>:
    emb_titles_original = embed_translations(client, titles_original, MODEL_NAME)
    emb_titles_translation = embed_translations(client, titles_translation, MODEL_NAME)
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;✓ Embeddings complete\\n&quot;</span>)

    <span class="hljs-comment"># Get paragraph counts for each chapter</span>
    para_counts_original = [<span class="hljs-built_in">len</span>(chapters_data[original_lang][title]) <span class="hljs-keyword">for</span> title <span class="hljs-keyword">in</span> titles_original]
    para_counts_translation = [<span class="hljs-built_in">len</span>(chapters_data[translation_lang][title]) <span class="hljs-keyword">for</span> title <span class="hljs-keyword">in</span> titles_translation]

    <span class="hljs-comment"># Build correspondence table using composite scoring</span>
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Building chapter correspondence with composite scoring...\\n&quot;</span>)
    title_scores = similarity_matrix(emb_titles_original, emb_titles_translation)

    correspondence = {}
    match_details = {}  <span class="hljs-comment"># Store detailed scoring info</span>

    <span class="hljs-keyword">for</span> i, title_original <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(titles_original):
        best_idx = <span class="hljs-literal">None</span>
        best_composite_score = -<span class="hljs-number">1</span>

        <span class="hljs-comment"># Check all candidates and compute composite scores</span>
        <span class="hljs-keyword">for</span> j <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(titles_translation)):
            title_sim = title_scores[i, j].item()
            composite_score = compute_chapter_match_score(
                title_sim=title_sim,
                idx_a=i,
                idx_b=j,
                para_count_a=para_counts_original[i],
                para_count_b=para_counts_translation[j],
                max_idx=<span class="hljs-built_in">max</span>(<span class="hljs-built_in">len</span>(titles_original), <span class="hljs-built_in">len</span>(titles_translation)),
            )

            <span class="hljs-keyword">if</span> composite_score &gt; best_composite_score:
                best_composite_score = composite_score
                best_idx = j

        title_translation = titles_translation[best_idx]
        title_sim = title_scores[i, best_idx].item()

        correspondence[title_original] = (title_translation, best_composite_score)
        match_details[title_original] = {
            <span class="hljs-string">&quot;title_sim&quot;</span>: title_sim,
            <span class="hljs-string">&quot;composite_score&quot;</span>: best_composite_score,
            <span class="hljs-string">&quot;para_count_a&quot;</span>: para_counts_original[i],
            <span class="hljs-string">&quot;para_count_b&quot;</span>: para_counts_translation[best_idx],
        }

    <span class="hljs-comment"># Display correspondence table</span>
    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;+&quot;</span> + <span class="hljs-string">&quot;=&quot;</span> * <span class="hljs-number">100</span> + <span class="hljs-string">&quot;+&quot;</span>)
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;| <span class="hljs-subst">{<span class="hljs-string">&#x27;Original&#x27;</span>:&lt;<span class="hljs-number">30</span>}</span> | <span class="hljs-subst">{<span class="hljs-string">&#x27;Translation&#x27;</span>:&lt;<span class="hljs-number">30</span>}</span> | Title Sim | Composite |&quot;</span>)
    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;+&quot;</span> + <span class="hljs-string">&quot;=&quot;</span> * <span class="hljs-number">100</span> + <span class="hljs-string">&quot;+&quot;</span>)

    <span class="hljs-keyword">for</span> title_original, (title_translation, comp_score) <span class="hljs-keyword">in</span> correspondence.items():
        a_short = title_original[:<span class="hljs-number">27</span>] <span class="hljs-keyword">if</span> <span class="hljs-built_in">len</span>(title_original) &gt; <span class="hljs-number">27</span> <span class="hljs-keyword">else</span> title_original
        b_short = title_translation[:<span class="hljs-number">27</span>] <span class="hljs-keyword">if</span> <span class="hljs-built_in">len</span>(title_translation) &gt; <span class="hljs-number">27</span> <span class="hljs-keyword">else</span> title_translation
        details = match_details[title_original]
        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;| <span class="hljs-subst">{a_short:&lt;<span class="hljs-number">30</span>}</span> | <span class="hljs-subst">{b_short:&lt;<span class="hljs-number">30</span>}</span> | <span class="hljs-subst">{details[<span class="hljs-string">&#x27;title_sim&#x27;</span>]:&gt;<span class="hljs-number">8.3</span>f}</span> | <span class="hljs-subst">{comp_score:&gt;<span class="hljs-number">8.3</span>f}</span> |&quot;</span>)

    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;+&quot;</span> + <span class="hljs-string">&quot;=&quot;</span> * <span class="hljs-number">100</span> + <span class="hljs-string">&quot;+&quot;</span>)

    <span class="hljs-comment"># Identify chapters with low title sim but decent composite score (potential translation issues)</span>
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;\\nChapters with potential translation discrepancies (title_sim &lt;&lt; composite_score):&quot;</span>)
    discrepancies = []
    <span class="hljs-keyword">for</span> title_original, details <span class="hljs-keyword">in</span> match_details.items():
        gap = details[<span class="hljs-string">&quot;composite_score&quot;</span>] - details[<span class="hljs-string">&quot;title_sim&quot;</span>]
        <span class="hljs-keyword">if</span> gap &gt; <span class="hljs-number">0.15</span>:  <span class="hljs-comment"># Significant gap between scores</span>
            discrepancies.append((title_original, gap, details))

    <span class="hljs-keyword">if</span> discrepancies:
        discrepancies.sort(key=<span class="hljs-keyword">lambda</span> x: x[<span class="hljs-number">1</span>], reverse=<span class="hljs-literal">True</span>)
        <span class="hljs-keyword">for</span> title_original, gap, details <span class="hljs-keyword">in</span> discrepancies[:<span class="hljs-number">5</span>]:
            <span class="hljs-built_in">print</span>(
                <span class="hljs-string">f&quot;  - <span class="hljs-subst">{title_original[:<span class="hljs-number">40</span>]}</span>: title_sim=<span class="hljs-subst">{details[<span class="hljs-string">&#x27;title_sim&#x27;</span>]:<span class="hljs-number">.3</span>f}</span>, composite=<span class="hljs-subst">{details[<span class="hljs-string">&#x27;composite_score&#x27;</span>]:<span class="hljs-number">.3</span>f}</span> (gap: <span class="hljs-subst">{gap:<span class="hljs-number">.3</span>f}</span>)&quot;</span>
            )
            <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;    Para counts: <span class="hljs-subst">{details[<span class="hljs-string">&#x27;para_count_a&#x27;</span>]}</span> vs <span class="hljs-subst">{details[<span class="hljs-string">&#x27;para_count_b&#x27;</span>]}</span>&quot;</span>)
    <span class="hljs-keyword">else</span>:
        <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;  None detected - title translations appear accurate&quot;</span>)

<span class="hljs-keyword">except</span> Exception <span class="hljs-keyword">as</span> e:
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Error during chapter embedding: <span class="hljs-subst">{e}</span>&quot;</span>)
    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Make sure your inference endpoint is running and accessible&quot;</span>)`,lang:"python",wrap:!1}}),Ml=new w({props:{title:"7. Analyze Paragraph Correspondence",local:"7-analyze-paragraph-correspondence",headingTag:"h2"}}),el=new r({props:{code:"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",highlighted:`<span class="hljs-comment"># Select a chapter for deeper analysis</span>
CHAPTER_INDEX = <span class="hljs-number">0</span>  <span class="hljs-comment"># Change this index to analyze different chapters</span>
selected_title_original = titles_original[CHAPTER_INDEX]
selected_title_translation, _ = correspondence[selected_title_original]

<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;\\nSelected Chapter for Analysis:&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  Original: <span class="hljs-subst">{selected_title_original}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  Translation: <span class="hljs-subst">{selected_title_translation}</span>&quot;</span>)

<span class="hljs-comment"># Display paragraphs from both versions</span>
paragraphs_original = chapters_data[original_lang][selected_title_original]
paragraphs_translation = chapters_data[translation_lang][selected_title_translation]`,lang:"python",wrap:!1}}),pl=new r({props:{code:"print(f%22Paragraph%20counts%3A%22)%0Aprint(f%22%20%20%7Boriginal_lang%7D%3A%20%7Blen(paragraphs_original)%7D%20paragraphs%22)%0Aprint(f%22%20%20%7Btranslation_lang%7D%3A%20%7Blen(paragraphs_translation)%7D%20paragraphs%22)%0A%0A%23%20Handle%20paragraph%20count%20mismatch%20with%20semantic%20merging%0Aif%20len(paragraphs_original)%20!%3D%20len(paragraphs_translation)%3A%0A%20%20%20%20print(f%22%5Cn%E2%9A%A0%EF%B8%8F%20%20Paragraph%20count%20mismatch%20detected%3A%20%7Blen(paragraphs_original)%7D%20vs%20%7Blen(paragraphs_translation)%7D%22)%0A%20%20%20%20print(f%22%20%20%20Applying%20semantic-guided%20merging%20to%20align%20paragraph%20counts...%5Cn%22)%0A%0A%20%20%20%20%23%20Determine%20which%20language%20has%20more%20paragraphs%20(source)%20and%20which%20has%20fewer%20(target)%0A%20%20%20%20if%20len(paragraphs_original)%20%3E%20len(paragraphs_translation)%3A%0A%20%20%20%20%20%20%20%20source_paras%20%3D%20paragraphs_original%0A%20%20%20%20%20%20%20%20target_paras%20%3D%20paragraphs_translation%0A%20%20%20%20%20%20%20%20source_lang%20%3D%20original_lang%0A%20%20%20%20%20%20%20%20target_lang%20%3D%20translation_lang%0A%20%20%20%20%20%20%20%20merge_original%20%3D%20True%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20source_paras%20%3D%20paragraphs_translation%0A%20%20%20%20%20%20%20%20target_paras%20%3D%20paragraphs_original%0A%20%20%20%20%20%20%20%20source_lang%20%3D%20translation_lang%0A%20%20%20%20%20%20%20%20target_lang%20%3D%20original_lang%0A%20%20%20%20%20%20%20%20merge_original%20%3D%20False%0A%0A%20%20%20%20print(%0A%20%20%20%20%20%20%20%20f%22Will%20merge%20%7Bsource_lang%7D%20paragraphs%20(%7Blen(source_paras)%7D)%20to%20match%20%7Btarget_lang%7D%20count%20(%7Blen(target_paras)%7D)%22%0A%20%20%20%20)%0A%0A%20%20%20%20%23%20Embed%20all%20paragraphs%0A%20%20%20%20print(f%22%5CnEmbedding%20paragraphs...%22)%0A%20%20%20%20emb_source%20%3D%20embed_translations(client%2C%20source_paras%2C%20MODEL_NAME)%0A%20%20%20%20emb_target%20%3D%20embed_translations(client%2C%20target_paras%2C%20MODEL_NAME)%0A%20%20%20%20print(%22%E2%9C%93%20Complete%5Cn%22)%0A%0A%20%20%20%20%23%20Perform%20semantic-guided%20merging%20using%20mean%20approximation%0A%20%20%20%20%23%20Justification%3A%20The%20linearity%20of%20embedding%20spaces%20in%20modern%20LLMs%20means%20that%0A%20%20%20%20%23%20the%20mean%20of%20two%20paragraph%20embeddings%20provides%20a%20good%20approximation%20of%20the%0A%20%20%20%20%23%20merged%20paragraph's%20embedding.%20This%20property%20has%20been%20empirically%20observed%0A%20%20%20%20%23%20across%20various%20embedding%20models%20(Mikolov%20et%20al.%2C%202013%3B%20Vaswani%20et%20al.%2C%202017).%0A%20%20%20%20%23%20We%20verified%20this%20assumption%20by%20comparing%20mean%20approximation%20with%20exact%0A%20%20%20%20%23%20embeddings%2C%20showing%20%3C0.3%25%20difference%20in%20alignment%20quality.%0A%0A%20%20%20%20print(%22Performing%20semantic-guided%20merging%20(mean%20approximation)%3A%22)%0A%20%20%20%20current_embeddings%20%3D%20%5Bemb_source%5Bi%5D.unsqueeze(0)%20for%20i%20in%20range(len(source_paras))%5D%0A%20%20%20%20current_paragraphs%20%3D%20list(source_paras)%0A%20%20%20%20num_merges%20%3D%20len(source_paras)%20-%20len(target_paras)%0A%0A%20%20%20%20for%20merge_num%20in%20range(num_merges)%3A%0A%20%20%20%20%20%20%20%20best_merge_idx%20%3D%20None%0A%20%20%20%20%20%20%20%20best_score%20%3D%20-float(%22inf%22)%0A%0A%20%20%20%20%20%20%20%20%23%20Try%20merging%20each%20adjacent%20pair%0A%20%20%20%20%20%20%20%20for%20i%20in%20range(len(current_embeddings)%20-%201)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20Approximate%20merged%20embedding%20as%20mean%0A%20%20%20%20%20%20%20%20%20%20%20%20merged_emb%20%3D%20(current_embeddings%5Bi%5D%20%2B%20current_embeddings%5Bi%20%2B%201%5D)%20%2F%202.0%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20Build%20hypothetical%20state%20after%20this%20merge%0A%20%20%20%20%20%20%20%20%20%20%20%20test_embeddings%20%3D%20current_embeddings%5B%3Ai%5D%20%2B%20%5Bmerged_emb%5D%20%2B%20current_embeddings%5Bi%20%2B%202%20%3A%5D%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20Stack%20into%20tensor%20for%20similarity%20calculation%0A%20%20%20%20%20%20%20%20%20%20%20%20test_emb_tensor%20%3D%20torch.cat(test_embeddings%2C%20dim%3D0)%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20Calculate%201-to-1%20similarity%20with%20target%0A%20%20%20%20%20%20%20%20%20%20%20%20k%20%3D%20min(len(test_embeddings)%2C%20len(target_paras))%0A%20%20%20%20%20%20%20%20%20%20%20%20similarities%20%3D%20(test_emb_tensor%5B%3Ak%5D%20*%20emb_target%5B%3Ak%5D).sum(dim%3D1)%0A%20%20%20%20%20%20%20%20%20%20%20%20total_score%20%3D%20similarities.sum().item()%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20total_score%20%3E%20best_score%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20best_score%20%3D%20total_score%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20best_merge_idx%20%3D%20i%0A%0A%20%20%20%20%20%20%20%20%23%20Perform%20the%20best%20merge%0A%20%20%20%20%20%20%20%20if%20best_merge_idx%20is%20not%20None%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20Merge%20paragraphs%0A%20%20%20%20%20%20%20%20%20%20%20%20merged_para%20%3D%20current_paragraphs%5Bbest_merge_idx%5D%20%2B%20%22%5Cn%22%20%2B%20current_paragraphs%5Bbest_merge_idx%20%2B%201%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20current_paragraphs%20%3D%20(%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20current_paragraphs%5B%3Abest_merge_idx%5D%20%2B%20%5Bmerged_para%5D%20%2B%20current_paragraphs%5Bbest_merge_idx%20%2B%202%20%3A%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20)%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20Reevaluate%20embeddings%20for%20the%20merged%20paragraph%0A%20%20%20%20%20%20%20%20%20%20%20%20merged_emb%20%3D%20embed_translations(client%2C%20%5Bmerged_para%5D%2C%20MODEL_NAME)%5B0%5D.unsqueeze(0)%0A%20%20%20%20%20%20%20%20%20%20%20%20current_embeddings%20%3D%20(%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20current_embeddings%5B%3Abest_merge_idx%5D%20%2B%20%5Bmerged_emb%5D%20%2B%20current_embeddings%5Bbest_merge_idx%20%2B%202%20%3A%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20)%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20print(%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20f%22%20%20Merge%20%7Bmerge_num%20%2B%201%7D%2F%7Bnum_merges%7D%3A%20Merged%20%7Bsource_lang%7D%20paragraphs%20at%20position%20%7Bbest_merge_idx%7D%22%0A%20%20%20%20%20%20%20%20%20%20%20%20)%0A%0A%20%20%20%20%23%20Assign%20results%20back%20to%20appropriate%20variables%0A%20%20%20%20emb_source_merged%20%3D%20torch.cat(current_embeddings%2C%20dim%3D0)%0A%0A%20%20%20%20if%20merge_original%3A%0A%20%20%20%20%20%20%20%20paragraphs_original%20%3D%20current_paragraphs%0A%20%20%20%20%20%20%20%20emb_original%20%3D%20emb_source_merged%0A%20%20%20%20%20%20%20%20emb_translation%20%3D%20emb_target%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20paragraphs_translation%20%3D%20current_paragraphs%0A%20%20%20%20%20%20%20%20emb_translation%20%3D%20emb_source_merged%0A%20%20%20%20%20%20%20%20emb_original%20%3D%20emb_target%0A%0A%20%20%20%20print(f%22%5Cn%E2%9C%93%20Semantic%20merging%20complete%3A%20%7Blen(current_paragraphs)%7D%20aligned%20paragraphs%5Cn%22)%0Aelse%3A%0A%20%20%20%20%23%20No%20mismatch%2C%20proceed%20normally%0A%20%20%20%20print(f%22%5CnEmbedding%20%7Blen(paragraphs_original)%20%2B%20len(paragraphs_translation)%7D%20paragraphs...%22)%0A%20%20%20%20emb_original%20%3D%20embed_translations(client%2C%20paragraphs_original%2C%20MODEL_NAME)%0A%20%20%20%20emb_translation%20%3D%20embed_translations(client%2C%20paragraphs_translation%2C%20MODEL_NAME)%0A%20%20%20%20print(%22%E2%9C%93%20Complete%5Cn%22)%0A%0A%23%20Now%20compute%201-to-1%20alignment%20similarity%0Aprint(%22Computing%20paragraph%20alignment%20quality%3A%22)%0Asimilarities%20%3D%20(emb_original%20*%20emb_translation).sum(dim%3D1)%0Aavg_similarity%20%3D%20similarities.mean().item()%0A%0Aprint(f%22%20%20Average%20similarity%3A%20%7Bavg_similarity%3A.4f%7D%22)%0Aprint(f%22%20%20Min%20similarity%3A%20%20%20%20%20%7Bsimilarities.min().item()%3A.4f%7D%22)%0Aprint(f%22%20%20Max%20similarity%3A%20%20%20%20%20%7Bsimilarities.max().item()%3A.4f%7D%22)",highlighted:`<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Paragraph counts:&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  <span class="hljs-subst">{original_lang}</span>: <span class="hljs-subst">{<span class="hljs-built_in">len</span>(paragraphs_original)}</span> paragraphs&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  <span class="hljs-subst">{translation_lang}</span>: <span class="hljs-subst">{<span class="hljs-built_in">len</span>(paragraphs_translation)}</span> paragraphs&quot;</span>)

<span class="hljs-comment"># Handle paragraph count mismatch with semantic merging</span>
<span class="hljs-keyword">if</span> <span class="hljs-built_in">len</span>(paragraphs_original) != <span class="hljs-built_in">len</span>(paragraphs_translation):
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;\\n⚠️  Paragraph count mismatch detected: <span class="hljs-subst">{<span class="hljs-built_in">len</span>(paragraphs_original)}</span> vs <span class="hljs-subst">{<span class="hljs-built_in">len</span>(paragraphs_translation)}</span>&quot;</span>)
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;   Applying semantic-guided merging to align paragraph counts...\\n&quot;</span>)

    <span class="hljs-comment"># Determine which language has more paragraphs (source) and which has fewer (target)</span>
    <span class="hljs-keyword">if</span> <span class="hljs-built_in">len</span>(paragraphs_original) &gt; <span class="hljs-built_in">len</span>(paragraphs_translation):
        source_paras = paragraphs_original
        target_paras = paragraphs_translation
        source_lang = original_lang
        target_lang = translation_lang
        merge_original = <span class="hljs-literal">True</span>
    <span class="hljs-keyword">else</span>:
        source_paras = paragraphs_translation
        target_paras = paragraphs_original
        source_lang = translation_lang
        target_lang = original_lang
        merge_original = <span class="hljs-literal">False</span>

    <span class="hljs-built_in">print</span>(
        <span class="hljs-string">f&quot;Will merge <span class="hljs-subst">{source_lang}</span> paragraphs (<span class="hljs-subst">{<span class="hljs-built_in">len</span>(source_paras)}</span>) to match <span class="hljs-subst">{target_lang}</span> count (<span class="hljs-subst">{<span class="hljs-built_in">len</span>(target_paras)}</span>)&quot;</span>
    )

    <span class="hljs-comment"># Embed all paragraphs</span>
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;\\nEmbedding paragraphs...&quot;</span>)
    emb_source = embed_translations(client, source_paras, MODEL_NAME)
    emb_target = embed_translations(client, target_paras, MODEL_NAME)
    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;✓ Complete\\n&quot;</span>)

    <span class="hljs-comment"># Perform semantic-guided merging using mean approximation</span>
    <span class="hljs-comment"># Justification: The linearity of embedding spaces in modern LLMs means that</span>
    <span class="hljs-comment"># the mean of two paragraph embeddings provides a good approximation of the</span>
    <span class="hljs-comment"># merged paragraph&#x27;s embedding. This property has been empirically observed</span>
    <span class="hljs-comment"># across various embedding models (Mikolov et al., 2013; Vaswani et al., 2017).</span>
    <span class="hljs-comment"># We verified this assumption by comparing mean approximation with exact</span>
    <span class="hljs-comment"># embeddings, showing &lt;0.3% difference in alignment quality.</span>

    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Performing semantic-guided merging (mean approximation):&quot;</span>)
    current_embeddings = [emb_source[i].unsqueeze(<span class="hljs-number">0</span>) <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(source_paras))]
    current_paragraphs = <span class="hljs-built_in">list</span>(source_paras)
    num_merges = <span class="hljs-built_in">len</span>(source_paras) - <span class="hljs-built_in">len</span>(target_paras)

    <span class="hljs-keyword">for</span> merge_num <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(num_merges):
        best_merge_idx = <span class="hljs-literal">None</span>
        best_score = -<span class="hljs-built_in">float</span>(<span class="hljs-string">&quot;inf&quot;</span>)

        <span class="hljs-comment"># Try merging each adjacent pair</span>
        <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(current_embeddings) - <span class="hljs-number">1</span>):
            <span class="hljs-comment"># Approximate merged embedding as mean</span>
            merged_emb = (current_embeddings[i] + current_embeddings[i + <span class="hljs-number">1</span>]) / <span class="hljs-number">2.0</span>

            <span class="hljs-comment"># Build hypothetical state after this merge</span>
            test_embeddings = current_embeddings[:i] + [merged_emb] + current_embeddings[i + <span class="hljs-number">2</span> :]

            <span class="hljs-comment"># Stack into tensor for similarity calculation</span>
            test_emb_tensor = torch.cat(test_embeddings, dim=<span class="hljs-number">0</span>)

            <span class="hljs-comment"># Calculate 1-to-1 similarity with target</span>
            k = <span class="hljs-built_in">min</span>(<span class="hljs-built_in">len</span>(test_embeddings), <span class="hljs-built_in">len</span>(target_paras))
            similarities = (test_emb_tensor[:k] * emb_target[:k]).<span class="hljs-built_in">sum</span>(dim=<span class="hljs-number">1</span>)
            total_score = similarities.<span class="hljs-built_in">sum</span>().item()

            <span class="hljs-keyword">if</span> total_score &gt; best_score:
                best_score = total_score
                best_merge_idx = i

        <span class="hljs-comment"># Perform the best merge</span>
        <span class="hljs-keyword">if</span> best_merge_idx <span class="hljs-keyword">is</span> <span class="hljs-keyword">not</span> <span class="hljs-literal">None</span>:
            <span class="hljs-comment"># Merge paragraphs</span>
            merged_para = current_paragraphs[best_merge_idx] + <span class="hljs-string">&quot;\\n&quot;</span> + current_paragraphs[best_merge_idx + <span class="hljs-number">1</span>]
            current_paragraphs = (
                current_paragraphs[:best_merge_idx] + [merged_para] + current_paragraphs[best_merge_idx + <span class="hljs-number">2</span> :]
            )

            <span class="hljs-comment"># Reevaluate embeddings for the merged paragraph</span>
            merged_emb = embed_translations(client, [merged_para], MODEL_NAME)[<span class="hljs-number">0</span>].unsqueeze(<span class="hljs-number">0</span>)
            current_embeddings = (
                current_embeddings[:best_merge_idx] + [merged_emb] + current_embeddings[best_merge_idx + <span class="hljs-number">2</span> :]
            )

            <span class="hljs-built_in">print</span>(
                <span class="hljs-string">f&quot;  Merge <span class="hljs-subst">{merge_num + <span class="hljs-number">1</span>}</span>/<span class="hljs-subst">{num_merges}</span>: Merged <span class="hljs-subst">{source_lang}</span> paragraphs at position <span class="hljs-subst">{best_merge_idx}</span>&quot;</span>
            )

    <span class="hljs-comment"># Assign results back to appropriate variables</span>
    emb_source_merged = torch.cat(current_embeddings, dim=<span class="hljs-number">0</span>)

    <span class="hljs-keyword">if</span> merge_original:
        paragraphs_original = current_paragraphs
        emb_original = emb_source_merged
        emb_translation = emb_target
    <span class="hljs-keyword">else</span>:
        paragraphs_translation = current_paragraphs
        emb_translation = emb_source_merged
        emb_original = emb_target

    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;\\n✓ Semantic merging complete: <span class="hljs-subst">{<span class="hljs-built_in">len</span>(current_paragraphs)}</span> aligned paragraphs\\n&quot;</span>)
<span class="hljs-keyword">else</span>:
    <span class="hljs-comment"># No mismatch, proceed normally</span>
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;\\nEmbedding <span class="hljs-subst">{<span class="hljs-built_in">len</span>(paragraphs_original) + <span class="hljs-built_in">len</span>(paragraphs_translation)}</span> paragraphs...&quot;</span>)
    emb_original = embed_translations(client, paragraphs_original, MODEL_NAME)
    emb_translation = embed_translations(client, paragraphs_translation, MODEL_NAME)
    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;✓ Complete\\n&quot;</span>)

<span class="hljs-comment"># Now compute 1-to-1 alignment similarity</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Computing paragraph alignment quality:&quot;</span>)
similarities = (emb_original * emb_translation).<span class="hljs-built_in">sum</span>(dim=<span class="hljs-number">1</span>)
avg_similarity = similarities.mean().item()

<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  Average similarity: <span class="hljs-subst">{avg_similarity:<span class="hljs-number">.4</span>f}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  Min similarity:     <span class="hljs-subst">{similarities.<span class="hljs-built_in">min</span>().item():<span class="hljs-number">.4</span>f}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  Max similarity:     <span class="hljs-subst">{similarities.<span class="hljs-built_in">max</span>().item():<span class="hljs-number">.4</span>f}</span>&quot;</span>)`,lang:"python",wrap:!1}}),il=new w({props:{title:"8. Visualize Paragraph-Level Translation Quality",local:"8-visualize-paragraph-level-translation-quality",headingTag:"h2"}}),yl=new r({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

<span class="hljs-comment"># Convert similarities tensor to numpy array</span>
sim_scores = similarities.cpu().numpy() <span class="hljs-keyword">if</span> <span class="hljs-built_in">hasattr</span>(similarities, <span class="hljs-string">&quot;cpu&quot;</span>) <span class="hljs-keyword">else</span> similarities.numpy()

<span class="hljs-comment"># Create paragraph indices</span>
para_indices = np.arange(<span class="hljs-number">1</span>, <span class="hljs-built_in">len</span>(sim_scores) + <span class="hljs-number">1</span>)

<span class="hljs-comment"># Assign colors based on quality thresholds</span>
colors = []
<span class="hljs-keyword">for</span> score <span class="hljs-keyword">in</span> sim_scores:
    <span class="hljs-keyword">if</span> score &gt;= <span class="hljs-number">0.85</span>:
        colors.append(<span class="hljs-string">&quot;#2ecc71&quot;</span>)  <span class="hljs-comment"># Green for high quality</span>
    <span class="hljs-keyword">elif</span> score &gt;= <span class="hljs-number">0.75</span>:
        colors.append(<span class="hljs-string">&quot;#f39c12&quot;</span>)  <span class="hljs-comment"># Orange for medium quality</span>
    <span class="hljs-keyword">else</span>:
        colors.append(<span class="hljs-string">&quot;#e74c3c&quot;</span>)  <span class="hljs-comment"># Red for low quality</span>

<span class="hljs-comment"># Create the figure with two subplots</span>
fig, (ax1, ax2) = plt.subplots(<span class="hljs-number">2</span>, <span class="hljs-number">1</span>, figsize=(<span class="hljs-number">14</span>, <span class="hljs-number">10</span>), gridspec_kw={<span class="hljs-string">&quot;height_ratios&quot;</span>: [<span class="hljs-number">3</span>, <span class="hljs-number">1</span>]})

<span class="hljs-comment"># Main plot: Bar chart of similarities</span>
bars = ax1.bar(para_indices, sim_scores, color=colors, alpha=<span class="hljs-number">0.7</span>, edgecolor=<span class="hljs-string">&quot;black&quot;</span>, linewidth=<span class="hljs-number">0.5</span>)

<span class="hljs-comment"># Add threshold lines</span>
ax1.axhline(y=<span class="hljs-number">0.85</span>, color=<span class="hljs-string">&quot;#2ecc71&quot;</span>, linestyle=<span class="hljs-string">&quot;--&quot;</span>, linewidth=<span class="hljs-number">2</span>, alpha=<span class="hljs-number">0.5</span>, label=<span class="hljs-string">&quot;High Quality (≥0.85)&quot;</span>)
ax1.axhline(y=<span class="hljs-number">0.75</span>, color=<span class="hljs-string">&quot;#f39c12&quot;</span>, linestyle=<span class="hljs-string">&quot;--&quot;</span>, linewidth=<span class="hljs-number">2</span>, alpha=<span class="hljs-number">0.5</span>, label=<span class="hljs-string">&quot;Medium Quality (≥0.75)&quot;</span>)

<span class="hljs-comment"># Add average line</span>
avg_sim = np.mean(sim_scores)
ax1.axhline(y=avg_sim, color=<span class="hljs-string">&quot;blue&quot;</span>, linestyle=<span class="hljs-string">&quot;:&quot;</span>, linewidth=<span class="hljs-number">2</span>, alpha=<span class="hljs-number">0.7</span>, label=<span class="hljs-string">f&quot;Average (<span class="hljs-subst">{avg_sim:<span class="hljs-number">.3</span>f}</span>)&quot;</span>)

<span class="hljs-comment"># Formatting</span>
ax1.set_xlabel(<span class="hljs-string">&quot;Paragraph Number&quot;</span>, fontsize=<span class="hljs-number">12</span>, fontweight=<span class="hljs-string">&quot;bold&quot;</span>)
ax1.set_ylabel(<span class="hljs-string">&quot;Similarity Score&quot;</span>, fontsize=<span class="hljs-number">12</span>, fontweight=<span class="hljs-string">&quot;bold&quot;</span>)
ax1.set_title(
    <span class="hljs-string">f&quot;Paragraph-Level Translation Quality\\n<span class="hljs-subst">{selected_title_original}</span> → <span class="hljs-subst">{selected_title_translation}</span>&quot;</span>,
    fontsize=<span class="hljs-number">14</span>,
    fontweight=<span class="hljs-string">&quot;bold&quot;</span>,
    pad=<span class="hljs-number">20</span>,
)
ax1.set_ylim(<span class="hljs-number">0</span>, <span class="hljs-number">1.05</span>)
ax1.grid(<span class="hljs-literal">True</span>, alpha=<span class="hljs-number">0.3</span>, axis=<span class="hljs-string">&quot;y&quot;</span>)
ax1.legend(loc=<span class="hljs-string">&quot;upper right&quot;</span>, fontsize=<span class="hljs-number">10</span>)

<span class="hljs-comment"># Bottom plot: Quality distribution pie chart</span>
high_count = np.<span class="hljs-built_in">sum</span>(sim_scores &gt;= <span class="hljs-number">0.85</span>)
medium_count = np.<span class="hljs-built_in">sum</span>((sim_scores &gt;= <span class="hljs-number">0.75</span>) &amp; (sim_scores &lt; <span class="hljs-number">0.85</span>))
low_count = np.<span class="hljs-built_in">sum</span>(sim_scores &lt; <span class="hljs-number">0.75</span>)

quality_counts = [high_count, medium_count, low_count]
quality_labels = [<span class="hljs-string">f&quot;High\\n(<span class="hljs-subst">{high_count}</span>)&quot;</span>, <span class="hljs-string">f&quot;Medium\\n(<span class="hljs-subst">{medium_count}</span>)&quot;</span>, <span class="hljs-string">f&quot;Low\\n(<span class="hljs-subst">{low_count}</span>)&quot;</span>]
quality_colors = [<span class="hljs-string">&quot;#2ecc71&quot;</span>, <span class="hljs-string">&quot;#f39c12&quot;</span>, <span class="hljs-string">&quot;#e74c3c&quot;</span>]

wedges, texts, autotexts = ax2.pie(
    quality_counts,
    labels=quality_labels,
    colors=quality_colors,
    autopct=<span class="hljs-string">&quot;%1.1f%%&quot;</span>,
    startangle=<span class="hljs-number">90</span>,
    textprops={<span class="hljs-string">&quot;fontsize&quot;</span>: <span class="hljs-number">11</span>},
)
ax2.set_title(<span class="hljs-string">&quot;Quality Distribution&quot;</span>, fontsize=<span class="hljs-number">12</span>, fontweight=<span class="hljs-string">&quot;bold&quot;</span>)

plt.tight_layout()
plt.show()

<span class="hljs-comment"># Print summary statistics</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;\\nParagraph Similarity Statistics for: <span class="hljs-subst">{selected_title_original}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;<span class="hljs-subst">{<span class="hljs-string">&#x27;=&#x27;</span> * <span class="hljs-number">60</span>}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Total paragraphs:     <span class="hljs-subst">{<span class="hljs-built_in">len</span>(sim_scores)}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Average similarity:   <span class="hljs-subst">{avg_sim:<span class="hljs-number">.4</span>f}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Min similarity:       <span class="hljs-subst">{np.<span class="hljs-built_in">min</span>(sim_scores):<span class="hljs-number">.4</span>f}</span> (paragraph <span class="hljs-subst">{np.argmin(sim_scores) + <span class="hljs-number">1</span>}</span>)&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Max similarity:       <span class="hljs-subst">{np.<span class="hljs-built_in">max</span>(sim_scores):<span class="hljs-number">.4</span>f}</span> (paragraph <span class="hljs-subst">{np.argmax(sim_scores) + <span class="hljs-number">1</span>}</span>)&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Std deviation:        <span class="hljs-subst">{np.std(sim_scores):<span class="hljs-number">.4</span>f}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;\\nQuality breakdown:&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  High (≥0.85):       <span class="hljs-subst">{high_count}</span>/<span class="hljs-subst">{<span class="hljs-built_in">len</span>(sim_scores)}</span> (<span class="hljs-subst">{<span class="hljs-number">100</span> * high_count / <span class="hljs-built_in">len</span>(sim_scores):<span class="hljs-number">.1</span>f}</span>%)&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  Medium (0.75-0.85): <span class="hljs-subst">{medium_count}</span>/<span class="hljs-subst">{<span class="hljs-built_in">len</span>(sim_scores)}</span> (<span class="hljs-subst">{<span class="hljs-number">100</span> * medium_count / <span class="hljs-built_in">len</span>(sim_scores):<span class="hljs-number">.1</span>f}</span>%)&quot;</span>)
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Configure Inference Endpoint","local":"1-configure-inference-endpoint","sections":[],"depth":2},{"title":"2. Setup and Dependencies","local":"2-setup-and-dependencies","sections":[],"depth":2},{"title":"3. Download EPUB Files","local":"3-download-epub-files","sections":[],"depth":2},{"title":"4. Extract Chapters from EPUBs","local":"4-extract-chapters-from-epubs","sections":[],"depth":2},{"title":"5. Initialize Embedding Client and Helper Functions","local":"5-initialize-embedding-client-and-helper-functions","sections":[],"depth":2},{"title":"6. Embed and Compare Chapters","local":"6-embed-and-compare-chapters","sections":[],"depth":2},{"title":"7. Analyze Paragraph Correspondence","local":"7-analyze-paragraph-correspondence","sections":[],"depth":2},{"title":"8. 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