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<title>quickmt vs.&nbsp;libretranslate</title>
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<h1 class="title">quickmt vs.&nbsp;libretranslate</h1>
</div>



<div class="quarto-title-meta">

    
  
    
  </div>
  


</header>


<p><code>libretranslate</code> is a popular (<a href="https://github.com/LibreTranslate/LibreTranslate">13k stars</a> on Github!) library enabling users to self-host (or do offline) translation for many <a href="https://www.argosopentech.com/argospm/index/">different languages</a>. The neural machine translation component of the system is provided by <a href="https://github.com/argosopentech/argos-translate"><code>argos translate</code></a> (5k stars on Github). <code>libretranslate</code> and <code>argostranslate</code> are both quite easy to get up and running with and the <code>libretranslate</code> user interface is quite slick but neither of these libraries are <em>quick</em>.</p>
<p>In this short article we examine the quality and speed of <code>libretranslate</code> in comparison with <code>quickmt</code>.</p>
<p>In all of the experiments we are translating the <a href="https://huggingface.co/datasets/Muennighoff/flores200/tree/main"><code>flores-devtest</code></a> test set. This is a popular general-domain machine translation test set for evaluation between any of 200 different languages including 1012 segments (sentences or short paragraphs).</p>
<p>All of the <code>quickmt</code> models are available on <a href="https://huggingface.co/collections/quickmt/quickmt-models">Huggingface</a>, and the <code>libretranslate</code> models are available from <a href="https://www.argosopentech.com/argospm/index/"><code>argos translate</code></a>.</p>
<p><code>libretranslate</code> and <code>quickmt</code> were both configured to run on an Nvidia RTX 4070s with batch size 32, and <code>libretranslate</code> was launched withe the following command:</p>
<pre class="{bash}"><code>ARGOS_DEVICE_TYPE=cuda libretranslate --load-only pt,de,fa,cs,tr,es,pl,bn,vi,fr,it,id,hu,th,lv,ko,ar,en,zh,ro,hi,he,ja,da,el,ur,ru </code></pre>
<section id="speed-comparison" class="level2">
<h2 class="anchored" data-anchor-id="speed-comparison">Speed Comparison</h2>
<p><code>quickmt</code> is considerably faster than <code>libretranslate</code>:</p>
<div id="f635ea6a" class="cell" data-execution_count="1">
<details class="code-fold">
<summary>Code</summary>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> plotly <span class="im">import</span> express <span class="im">as</span> px</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>df_data <span class="op">=</span> {<span class="st">'index'</span>: {<span class="dv">0</span>: <span class="dv">0</span>, <span class="dv">1</span>: <span class="dv">1</span>, <span class="dv">2</span>: <span class="dv">2</span>, <span class="dv">3</span>: <span class="dv">3</span>, <span class="dv">4</span>: <span class="dv">4</span>, <span class="dv">5</span>: <span class="dv">5</span>, <span class="dv">6</span>: <span class="dv">6</span>, <span class="dv">7</span>: <span class="dv">7</span>, <span class="dv">8</span>: <span class="dv">8</span>, <span class="dv">9</span>: <span class="dv">9</span>, <span class="dv">10</span>: <span class="dv">10</span>, <span class="dv">11</span>: <span class="dv">11</span>, <span class="dv">12</span>: <span class="dv">12</span>, <span class="dv">13</span>: <span class="dv">13</span>, <span class="dv">14</span>: <span class="dv">14</span>, <span class="dv">15</span>: <span class="dv">15</span>, <span class="dv">16</span>: <span class="dv">16</span>, <span class="dv">17</span>: <span class="dv">17</span>, <span class="dv">18</span>: <span class="dv">18</span>, <span class="dv">19</span>: <span class="dv">19</span>, <span class="dv">20</span>: <span class="dv">20</span>, <span class="dv">21</span>: <span class="dv">21</span>, <span class="dv">22</span>: <span class="dv">22</span>, <span class="dv">23</span>: <span class="dv">23</span>, <span class="dv">24</span>: <span class="dv">24</span>, <span class="dv">25</span>: <span class="dv">25</span>, <span class="dv">26</span>: <span class="dv">26</span>, <span class="dv">27</span>: <span class="dv">27</span>, <span class="dv">28</span>: <span class="dv">28</span>, <span class="dv">29</span>: <span class="dv">29</span>, <span class="dv">30</span>: <span class="dv">30</span>, <span class="dv">31</span>: <span class="dv">31</span>, <span class="dv">32</span>: <span class="dv">32</span>, <span 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class="fl">78.31</span>, <span class="dv">79</span>: <span class="fl">76.04</span>, <span class="dv">80</span>: <span class="fl">77.92</span>, <span class="dv">81</span>: <span class="fl">86.71</span>, <span class="dv">82</span>: <span class="fl">85.81</span>, <span class="dv">83</span>: <span class="fl">85.02</span>, <span class="dv">84</span>: <span class="fl">86.18</span>, <span class="dv">85</span>: <span class="fl">85.17</span>, <span class="dv">86</span>: <span class="fl">86.6</span>, <span class="dv">87</span>: <span class="fl">85.84</span>, <span class="dv">88</span>: <span class="fl">87.38</span>, <span class="dv">89</span>: <span class="fl">77.89</span>, <span class="dv">90</span>: <span class="fl">78.78</span>, <span class="dv">91</span>: <span class="fl">88.77</span>, <span class="dv">92</span>: <span class="fl">87.28</span>, <span class="dv">93</span>: <span class="fl">86.44</span>, <span class="dv">94</span>: <span class="fl">88.32</span>, <span class="dv">95</span>: <span class="fl">88.31</span>, <span class="dv">96</span>: <span class="fl">85.97</span>, <span class="dv">97</span>: <span class="fl">76.92</span>, <span class="dv">98</span>: <span class="fl">74.34</span>, <span class="dv">99</span>: <span class="fl">81.26</span>, <span class="dv">100</span>: <span class="fl">80.39</span>, <span class="dv">101</span>: <span class="fl">65.69</span>, <span class="dv">102</span>: <span class="fl">69.6</span>}, <span class="st">'system'</span>: {<span class="dv">0</span>: <span class="st">'quickmt'</span>, <span class="dv">1</span>: <span class="st">'quickmt'</span>, <span class="dv">2</span>: <span class="st">'quickmt'</span>, <span class="dv">3</span>: <span class="st">'quickmt'</span>, <span class="dv">4</span>: <span class="st">'quickmt'</span>, <span class="dv">5</span>: <span class="st">'quickmt'</span>, <span class="dv">6</span>: <span class="st">'quickmt'</span>, <span class="dv">7</span>: <span class="st">'quickmt'</span>, <span 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<span class="dv">24</span>: <span class="st">'quickmt'</span>, <span class="dv">25</span>: <span class="st">'quickmt'</span>, <span class="dv">26</span>: <span class="st">'quickmt'</span>, <span class="dv">27</span>: <span class="st">'quickmt'</span>, <span class="dv">28</span>: <span class="st">'quickmt'</span>, <span class="dv">29</span>: <span class="st">'quickmt'</span>, <span class="dv">30</span>: <span class="st">'quickmt'</span>, <span class="dv">31</span>: <span class="st">'quickmt'</span>, <span class="dv">32</span>: <span class="st">'quickmt'</span>, <span class="dv">33</span>: <span class="st">'quickmt'</span>, <span class="dv">34</span>: <span class="st">'quickmt'</span>, <span class="dv">35</span>: <span class="st">'quickmt'</span>, <span class="dv">36</span>: <span class="st">'quickmt'</span>, <span class="dv">37</span>: <span class="st">'quickmt'</span>, <span class="dv">38</span>: <span class="st">'quickmt'</span>, <span class="dv">39</span>: <span class="st">'quickmt'</span>, <span class="dv">40</span>: <span class="st">'quickmt'</span>, <span class="dv">41</span>: <span class="st">'quickmt'</span>, <span class="dv">42</span>: <span class="st">'quickmt'</span>, <span class="dv">43</span>: <span class="st">'quickmt'</span>, <span class="dv">44</span>: <span class="st">'quickmt'</span>, <span class="dv">45</span>: <span class="st">'quickmt'</span>, <span class="dv">46</span>: <span class="st">'quickmt'</span>, <span class="dv">47</span>: <span class="st">'quickmt'</span>, <span class="dv">48</span>: <span class="st">'quickmt'</span>, <span class="dv">49</span>: <span class="st">'quickmt'</span>, <span class="dv">50</span>: <span class="st">'quickmt'</span>, <span class="dv">51</span>: <span class="st">'quickmt'</span>, <span class="dv">52</span>: <span class="st">'libretranslate'</span>, <span class="dv">53</span>: <span class="st">'libretranslate'</span>, <span class="dv">54</span>: <span class="st">'libretranslate'</span>, <span class="dv">55</span>: <span class="st">'libretranslate'</span>, <span class="dv">56</span>: <span class="st">'libretranslate'</span>, <span class="dv">57</span>: <span class="st">'libretranslate'</span>, <span class="dv">58</span>: <span class="st">'libretranslate'</span>, <span class="dv">59</span>: <span class="st">'libretranslate'</span>, <span class="dv">60</span>: <span class="st">'libretranslate'</span>, <span class="dv">61</span>: <span class="st">'libretranslate'</span>, <span class="dv">62</span>: <span class="st">'libretranslate'</span>, <span class="dv">63</span>: <span class="st">'libretranslate'</span>, <span class="dv">64</span>: <span class="st">'libretranslate'</span>, <span class="dv">65</span>: <span class="st">'libretranslate'</span>, <span class="dv">66</span>: <span class="st">'libretranslate'</span>, <span class="dv">67</span>: <span class="st">'libretranslate'</span>, <span class="dv">68</span>: <span class="st">'libretranslate'</span>, <span 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class="dv">83</span>: <span class="st">'libretranslate'</span>, <span class="dv">84</span>: <span class="st">'libretranslate'</span>, <span class="dv">85</span>: <span class="st">'libretranslate'</span>, <span class="dv">86</span>: <span class="st">'libretranslate'</span>, <span class="dv">87</span>: <span class="st">'libretranslate'</span>, <span class="dv">88</span>: <span class="st">'libretranslate'</span>, <span class="dv">89</span>: <span class="st">'libretranslate'</span>, <span class="dv">90</span>: <span class="st">'libretranslate'</span>, <span class="dv">91</span>: <span class="st">'libretranslate'</span>, <span class="dv">92</span>: <span class="st">'libretranslate'</span>, <span class="dv">93</span>: <span class="st">'libretranslate'</span>, <span class="dv">94</span>: <span class="st">'libretranslate'</span>, <span class="dv">95</span>: <span class="st">'libretranslate'</span>, <span class="dv">96</span>: <span class="st">'libretranslate'</span>, <span class="dv">97</span>: <span class="st">'libretranslate'</span>, <span class="dv">98</span>: <span class="st">'libretranslate'</span>, <span class="dv">99</span>: <span class="st">'libretranslate'</span>, <span class="dv">100</span>: <span class="st">'libretranslate'</span>, <span class="dv">101</span>: <span class="st">'libretranslate'</span>, <span class="dv">102</span>: <span class="st">'libretranslate'</span>}}</span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(df_data)</span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a>df[[<span class="st">"system"</span>, <span class="st">"time"</span>]].groupby(<span class="st">"system"</span>).mean()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</details>
<div class="cell-output cell-output-display" data-execution_count="1">
<div>


<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
<thead>
<tr class="header">
<th data-quarto-table-cell-role="th"></th>
<th data-quarto-table-cell-role="th">time</th>
</tr>
<tr class="even">
<th data-quarto-table-cell-role="th">system</th>
<th data-quarto-table-cell-role="th"></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<th data-quarto-table-cell-role="th">libretranslate</th>
<td>51.787339</td>
</tr>
<tr class="even">
<th data-quarto-table-cell-role="th">quickmt</th>
<td>1.216358</td>
</tr>
</tbody>
</table>

</div>
</div>
</div>
<p>It takes just over 1 second to translate 1000 sentences with <code>quickmt</code>, and more than <strong>50 seconds</strong> using <code>libretranslate</code>! The <code>libretranslate</code> models were warmed up (loaded) before starting the timer.</p>
<section id="why-is-there-such-a-large-difference-in-speed" class="level3">
<h3 class="anchored" data-anchor-id="why-is-there-such-a-large-difference-in-speed">Why is there such a large difference in speed?</h3>
<p>Both <code>libretranslate</code> and <code>quickmt</code> use <code>ctranslate2</code> for translation and <code>sentencepiece</code> for tokenization under the hood, but <code>libretranslate</code> uses <a href="https://github.com/stanfordnlp/stanza"><code>stanza</code></a> for sentence splitting while <code>quickmt</code> uses <a href="https://github.com/microsoft/BlingFire"><code>blingfire</code></a>. While <code>blingfire</code> is not a <a href="https://arxiv.org/abs/2010.09657">particularly <em>accurate</em></a> sentence segmenter, it is dramatically faster than <a href="https://github.com/nipunsadvilkar/pySBD?tab=readme-ov-file">other</a> <a href="https://github.com/segment-any-text/wtpsplit">methods</a>.</p>
<p>Another difference between <code>quickmt</code> and <code>libretranslate</code> is that our models were designed to be quick by having a small number of decoder layers. Most of the <code>libretranslate</code> models are re-packaged Opus-MT models which have 6 decoder layers.</p>
<p>How does <code>quickmt</code> compare to <code>libretranslate</code> in terms of translation quality?</p>
</section>
</section>
<section id="quality-comparison" class="level2">
<h2 class="anchored" data-anchor-id="quality-comparison">Quality Comparison</h2>
<p>We use <code>chrf</code> was calculated with <a href="https://github.com/mjpost/sacrebleu"><code>sacrebleu</code></a> and <code>comet</code> with the <a href="https://github.com/Unbabel/COMET"><code>comet</code></a> library and the default model (comet22).</p>
<p>First let’s take a look at translation from the languages supported by <code>quickmt</code> into English:</p>
<div id="84016fc4" class="cell" data-execution_count="2">
<details class="code-fold">
<summary>Code</summary>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>px.bar(</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>    df[df.src_lang <span class="op">!=</span> <span class="st">"en"</span>],</span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>    x<span class="op">=</span><span class="st">"src_lang"</span>,</span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>    y<span class="op">=</span><span class="st">"comet22"</span>,</span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>    color<span class="op">=</span><span class="st">"system"</span>,</span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>    barmode<span class="op">=</span><span class="st">"group"</span>,</span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>    title<span class="op">=</span><span class="st">"XX-&gt;EN Machine Translation Quality - comet22 measure"</span>,</span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a>    range_y<span class="op">=</span>[<span class="dv">70</span>, <span class="dv">95</span>],</span>
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>    height<span class="op">=</span><span class="dv">500</span></span>
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</details>
<div class="cell-output cell-output-display">
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<details class="code-fold">
<summary>Code</summary>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>px.bar(</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>    df[df.src_lang <span class="op">!=</span> <span class="st">"en"</span>],</span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>    x<span class="op">=</span><span class="st">"src_lang"</span>,</span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>    y<span class="op">=</span><span class="st">"chrf2"</span>,</span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>    color<span class="op">=</span><span class="st">"system"</span>,</span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>    barmode<span class="op">=</span><span class="st">"group"</span>,</span>
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>    title<span class="op">=</span><span class="st">"XX-&gt;EN Machine Translation Quality - chrf measure"</span>,</span>
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a>    height<span class="op">=</span><span class="dv">500</span></span>
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
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Machine Translation Quality - chrf measure"},"barmode":"group","height":500},                        {"responsive": true}                    ).then(function(){
                            
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<p>For both <code>chrf</code> and <code>comet22</code> and for all languages, with the notable exception of Russian (85.96 comet22 for <code>quickmt</code> vs.&nbsp;86.44 for <code>libretranslate</code>), <code>quickmt</code> is higher quality than <code>libretranslate</code>. The differences are <strong>huge</strong> for Korean (86.01 vs 72.46 comet22), Turkish (88.99 vs.&nbsp;78.82 comet22), japanese (87.24 vs.&nbsp;76.04 comet22), Thai (87.15 vs.&nbsp;77.79 comet22) and Bengali (86.99 vs.&nbsp;76.92 comet22).</p>
<p>On average, <code>quickmt</code> is 4.95 higher in comet22 and 7.14 higher in chrf2 than <code>libretranslate</code> for translation into English:</p>
<div id="479ccc51" class="cell" data-execution_count="4">
<details class="code-fold">
<summary>Code</summary>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>df[df.tgt_lang<span class="op">==</span><span class="st">"en"</span>][[<span class="st">"system"</span>, <span class="st">"comet22"</span>]].groupby(<span class="st">"system"</span>).mean()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</details>
<div class="cell-output cell-output-display" data-execution_count="4">
<div>


<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
<thead>
<tr class="header">
<th data-quarto-table-cell-role="th"></th>
<th data-quarto-table-cell-role="th">comet22</th>
</tr>
<tr class="even">
<th data-quarto-table-cell-role="th">system</th>
<th data-quarto-table-cell-role="th"></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<th data-quarto-table-cell-role="th">libretranslate</th>
<td>82.599600</td>
</tr>
<tr class="even">
<th data-quarto-table-cell-role="th">quickmt</th>
<td>87.549615</td>
</tr>
</tbody>
</table>

</div>
</div>
</div>
<div id="29a775e8" class="cell" data-execution_count="5">
<details class="code-fold">
<summary>Code</summary>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>df[df.tgt_lang<span class="op">==</span><span class="st">"en"</span>][[<span class="st">"system"</span>, <span class="st">"chrf2"</span>]].groupby(<span class="st">"system"</span>).mean()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</details>
<div class="cell-output cell-output-display" data-execution_count="5">
<div>


<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
<thead>
<tr class="header">
<th data-quarto-table-cell-role="th"></th>
<th data-quarto-table-cell-role="th">chrf2</th>
</tr>
<tr class="even">
<th data-quarto-table-cell-role="th">system</th>
<th data-quarto-table-cell-role="th"></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<th data-quarto-table-cell-role="th">libretranslate</th>
<td>56.313600</td>
</tr>
<tr class="even">
<th data-quarto-table-cell-role="th">quickmt</th>
<td>63.457308</td>
</tr>
</tbody>
</table>

</div>
</div>
</div>
<p>Next we look at translation from English into the languages supported by <code>quickmt</code>:</p>
<div id="8a66d0df" class="cell" data-execution_count="6">
<details class="code-fold">
<summary>Code</summary>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>px.bar(</span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>    df[df.src_lang <span class="op">==</span> <span class="st">"en"</span>],</span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>    x<span class="op">=</span><span class="st">"tgt_lang"</span>,</span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>    y<span class="op">=</span><span class="st">"comet22"</span>,</span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>    color<span class="op">=</span><span class="st">"system"</span>,</span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a>    barmode<span class="op">=</span><span class="st">"group"</span>,</span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a>    title<span class="op">=</span><span class="st">"EN-&gt;XX Machine Translation Quality - comet22 measure"</span>,</span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a>    range_y<span class="op">=</span>[<span class="dv">70</span>, <span class="dv">95</span>],</span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a>    height<span class="op">=</span><span class="dv">500</span></span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</details>
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<details class="code-fold">
<summary>Code</summary>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>px.bar(</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>    df[df.src_lang <span class="op">==</span> <span class="st">"en"</span>],</span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>    x<span class="op">=</span><span class="st">"tgt_lang"</span>,</span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>    y<span class="op">=</span><span class="st">"chrf2"</span>,</span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>    color<span class="op">=</span><span class="st">"system"</span>,</span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>    barmode<span class="op">=</span><span class="st">"group"</span>,</span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>    title<span class="op">=</span><span class="st">"EN-&gt;XX Machine Translation Quality - chrf2 measure"</span>,</span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>    height<span class="op">=</span><span class="dv">500</span></span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
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<p>The pattern here is similar - <code>quickmt</code> is higher-quality than <code>libretranslate</code> for all languages (except Russian!), sometimes dramatically but by an average of 4.97 comet22 and 6.38 chrf2.</p>
<div id="20b7bb4f" class="cell" data-execution_count="8">
<details class="code-fold">
<summary>Code</summary>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>df[df.tgt_lang<span class="op">!=</span><span class="st">"en"</span>][[<span class="st">"system"</span>, <span class="st">"comet22"</span>]].groupby(<span class="st">"system"</span>).mean()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</details>
<div class="cell-output cell-output-display" data-execution_count="8">
<div>


<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
<thead>
<tr class="header">
<th data-quarto-table-cell-role="th"></th>
<th data-quarto-table-cell-role="th">comet22</th>
</tr>
<tr class="even">
<th data-quarto-table-cell-role="th">system</th>
<th data-quarto-table-cell-role="th"></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<th data-quarto-table-cell-role="th">libretranslate</th>
<td>81.841538</td>
</tr>
<tr class="even">
<th data-quarto-table-cell-role="th">quickmt</th>
<td>86.808846</td>
</tr>
</tbody>
</table>

</div>
</div>
</div>
<div id="0c5249cc" class="cell" data-execution_count="9">
<details class="code-fold">
<summary>Code</summary>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>df[df.tgt_lang<span class="op">!=</span><span class="st">"en"</span>][[<span class="st">"system"</span>, <span class="st">"chrf2"</span>]].groupby(<span class="st">"system"</span>).mean()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</details>
<div class="cell-output cell-output-display" data-execution_count="9">
<div>


<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
<thead>
<tr class="header">
<th data-quarto-table-cell-role="th"></th>
<th data-quarto-table-cell-role="th">chrf2</th>
</tr>
<tr class="even">
<th data-quarto-table-cell-role="th">system</th>
<th data-quarto-table-cell-role="th"></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<th data-quarto-table-cell-role="th">libretranslate</th>
<td>51.745000</td>
</tr>
<tr class="even">
<th data-quarto-table-cell-role="th">quickmt</th>
<td>58.123077</td>
</tr>
</tbody>
</table>

</div>
</div>
</div>
</section>
<section id="conclusion" class="level2">
<h2 class="anchored" data-anchor-id="conclusion">Conclusion</h2>
<p><code>quickmt</code> is more than 40 times faster than <code>libretranslate</code> (on a RTX 4070s GPU) and quite a bit higher quality (for all languages except Russian).</p>
<p>You can give <code>quickmt</code> a try on <a href="https://huggingface.co/spaces/quickmt/QuickMT-Demo">Huggingface Spaces</a>, download the models from <a href="https://huggingface.co/collections/quickmt/quickmt-models">Huggingface</a> and make it your own by forking our <a href="https://github.com/quickmt/quickmt">Github repository</a>. Thanks for reading!</p>
</section>

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