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text.core.html
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---
title: Text core
keywords: fastai
sidebar: home_sidebar
summary: "Basic function to preprocess text before assembling it in a [`DataBunch`](/data.core.html#DataBunch)."
---
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">nbdev.showdoc</span> <span class="k">import</span> <span class="o">*</span>
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<h2 id="Preprocessing-rules">Preprocessing rules<a class="anchor-link" href="#Preprocessing-rules">¶</a></h2>
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<p>The following are rules applied to texts before or after it's tokenized.</p>
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<h4 id="spec_add_spaces" class="doc_header"><code>spec_add_spaces</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L26" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>spec_add_spaces</code>(<strong><code>t</code></strong>)</p>
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<p>Add spaces around / and #</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">(</span><span class="n">spec_add_spaces</span><span class="p">(</span><span class="s1">'#fastai'</span><span class="p">),</span> <span class="s1">' # fastai'</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">spec_add_spaces</span><span class="p">(</span><span class="s1">'/fastai'</span><span class="p">),</span> <span class="s1">' / fastai'</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">spec_add_spaces</span><span class="p">(</span><span class="s1">'</span><span class="se">\\</span><span class="s1">fastai'</span><span class="p">),</span> <span class="s1">' </span><span class="se">\\</span><span class="s1"> fastai'</span><span class="p">)</span>
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<h4 id="rm_useless_spaces" class="doc_header"><code>rm_useless_spaces</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L33" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>rm_useless_spaces</code>(<strong><code>t</code></strong>)</p>
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<p>Remove multiple spaces</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">(</span><span class="n">rm_useless_spaces</span><span class="p">(</span><span class="s1">'a b c'</span><span class="p">),</span> <span class="s1">'a b c'</span><span class="p">)</span>
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<h4 id="replace_rep" class="doc_header"><code>replace_rep</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L40" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>replace_rep</code>(<strong><code>t</code></strong>)</p>
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<p>Replace repetitions at the character level: cccc -- TK_REP 4 c</p>
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<p>It starts replacing at 3 repetitions of the same character or more.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">(</span><span class="n">replace_rep</span><span class="p">(</span><span class="s1">'aa'</span><span class="p">),</span> <span class="s1">'aa'</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">replace_rep</span><span class="p">(</span><span class="s1">'aaaa'</span><span class="p">),</span> <span class="n">f</span><span class="s1">' </span><span class="si">{TK_REP}</span><span class="s1"> 4 a '</span><span class="p">)</span>
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<h4 id="replace_wrep" class="doc_header"><code>replace_wrep</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L51" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>replace_wrep</code>(<strong><code>t</code></strong>)</p>
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<p>Replace word repetitions: word word word word -- TK_WREP 4 word</p>
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<p>It starts replacing at 3 repetitions of the same word or more.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">(</span><span class="n">replace_wrep</span><span class="p">(</span><span class="s1">'ah ah'</span><span class="p">),</span> <span class="s1">'ah ah'</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">replace_wrep</span><span class="p">(</span><span class="s1">'ah ah ah'</span><span class="p">),</span> <span class="n">f</span><span class="s1">' </span><span class="si">{TK_WREP}</span><span class="s1"> 3 ah '</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">replace_wrep</span><span class="p">(</span><span class="s1">'ah ah ah ah'</span><span class="p">),</span> <span class="n">f</span><span class="s1">' </span><span class="si">{TK_WREP}</span><span class="s1"> 4 ah '</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">replace_wrep</span><span class="p">(</span><span class="s1">'ah ah ah ah '</span><span class="p">),</span> <span class="n">f</span><span class="s1">' </span><span class="si">{TK_WREP}</span><span class="s1"> 4 ah '</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">replace_wrep</span><span class="p">(</span><span class="s1">'ah ah ah ah.'</span><span class="p">),</span> <span class="n">f</span><span class="s1">' </span><span class="si">{TK_WREP}</span><span class="s1"> 4 ah .'</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">replace_wrep</span><span class="p">(</span><span class="s1">'ah ah ahi'</span><span class="p">),</span> <span class="n">f</span><span class="s1">'ah ah ahi'</span><span class="p">)</span>
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<h4 id="fix_html" class="doc_header"><code>fix_html</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L59" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>fix_html</code>(<strong><code>x</code></strong>)</p>
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<p>Various messy things we've seen in documents</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">(</span><span class="n">fix_html</span><span class="p">(</span><span class="s1">'#39;bli#146;'</span><span class="p">),</span> <span class="s2">"'bli'"</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">fix_html</span><span class="p">(</span><span class="s1">'Sarah amp; Duck...'</span><span class="p">),</span> <span class="s1">'Sarah & Duck …'</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">fix_html</span><span class="p">(</span><span class="s1">'a nbsp; #36;'</span><span class="p">),</span> <span class="s1">'a $'</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">fix_html</span><span class="p">(</span><span class="s1">'</span><span class="se">\\</span><span class="s1">" <unk>'</span><span class="p">),</span> <span class="n">f</span><span class="s1">'" </span><span class="si">{UNK}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">fix_html</span><span class="p">(</span><span class="s1">'quot; @.@ @-@ '</span><span class="p">),</span> <span class="s2">"' .-"</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">fix_html</span><span class="p">(</span><span class="s1">'<br />text</span><span class="se">\\</span><span class="s1">n'</span><span class="p">),</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">text</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
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<h4 id="replace_all_caps" class="doc_header"><code>replace_all_caps</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L70" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>replace_all_caps</code>(<strong><code>t</code></strong>)</p>
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<p>Replace tokens in ALL CAPS by their lower version and add <code>TK_UP</code> before.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">(</span><span class="n">replace_all_caps</span><span class="p">(</span><span class="s2">"I'M SHOUTING"</span><span class="p">),</span> <span class="n">f</span><span class="s2">"</span><span class="si">{TK_UP}</span><span class="s2"> i'm </span><span class="si">{TK_UP}</span><span class="s2"> shouting"</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">replace_all_caps</span><span class="p">(</span><span class="s2">"I'm speaking normally"</span><span class="p">),</span> <span class="s2">"I'm speaking normally"</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">replace_all_caps</span><span class="p">(</span><span class="s2">"I am speaking normally"</span><span class="p">),</span> <span class="s2">"i am speaking normally"</span><span class="p">)</span>
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<h4 id="replace_maj" class="doc_header"><code>replace_maj</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L81" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>replace_maj</code>(<strong><code>t</code></strong>)</p>
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<p>Replace tokens in ALL CAPS by their lower version and add <code>TK_UP</code> before.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">(</span><span class="n">replace_maj</span><span class="p">(</span><span class="s2">"Jeremy Howard"</span><span class="p">),</span> <span class="n">f</span><span class="s1">'</span><span class="si">{TK_MAJ}</span><span class="s1"> jeremy </span><span class="si">{TK_MAJ}</span><span class="s1"> howard'</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">replace_maj</span><span class="p">(</span><span class="s2">"I don't think there is any maj here"</span><span class="p">),</span> <span class="p">(</span><span class="s2">"i don't think there is any maj here"</span><span class="p">),)</span>
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<h4 id="lowercase" class="doc_header"><code>lowercase</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L89" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>lowercase</code>(<strong><code>t</code></strong>, <strong><code>add_bos</code></strong>=<em><code>True</code></em>, <strong><code>add_eos</code></strong>=<em><code>False</code></em>)</p>
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<p>Converts <code>t</code> to lowercase</p>
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<h4 id="replace_space" class="doc_header"><code>replace_space</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L94" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>replace_space</code>(<strong><code>t</code></strong>)</p>
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<p>Replace embedded spaces in a token with unicode line char to allow for split/join</p>
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<h2 id="Tokenizing">Tokenizing<a class="anchor-link" href="#Tokenizing">¶</a></h2>
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<p>A tokenizer is a class that must implement a <code>pipe</code> method. This <code>pipe</code> method receives a generator of texts and must return a generator with their tokenized versions. Here is the most basic example:</p>
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<h3 id="BaseTokenizer" class="doc_header"><code>class</code> <code>BaseTokenizer</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L105" class="source_link" style="float:right">[source]</a></h3><blockquote><p><code>BaseTokenizer</code>(<strong><code>split_char</code></strong>=<em><code>' '</code></em>, <strong>**<code>kwargs</code></strong>)</p>
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<p>Basic tokenizer that just splits on spaces</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tok</span> <span class="o">=</span> <span class="n">BaseTokenizer</span><span class="p">()</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tok</span><span class="p">([</span><span class="s2">"This is a text"</span><span class="p">]):</span> <span class="n">test_eq</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="p">[</span><span class="s2">"This"</span><span class="p">,</span> <span class="s2">"is"</span><span class="p">,</span> <span class="s2">"a"</span><span class="p">,</span> <span class="s2">"text"</span><span class="p">])</span>
<span class="n">tok</span> <span class="o">=</span> <span class="n">BaseTokenizer</span><span class="p">(</span><span class="s1">'x'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tok</span><span class="p">([</span><span class="s2">"This is a text"</span><span class="p">]):</span> <span class="n">test_eq</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="p">[</span><span class="s2">"This is a te"</span><span class="p">,</span> <span class="s2">"t"</span><span class="p">])</span>
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<h3 id="SpacyTokenizer" class="doc_header"><code>class</code> <code>SpacyTokenizer</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L111" class="source_link" style="float:right">[source]</a></h3><blockquote><p><code>SpacyTokenizer</code>(<strong><code>lang</code></strong>=<em><code>'en'</code></em>, <strong><code>special_toks</code></strong>=<em><code>None</code></em>, <strong><code>buf_sz</code></strong>=<em><code>5000</code></em>)</p>
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<p>Spacy tokenizer for <code>lang</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tok</span> <span class="o">=</span> <span class="n">SpacyTokenizer</span><span class="p">()</span>
<span class="n">inp</span><span class="p">,</span><span class="n">exp</span> <span class="o">=</span> <span class="s2">"This isn't the easiest text."</span><span class="p">,[</span><span class="s2">"This"</span><span class="p">,</span> <span class="s2">"is"</span><span class="p">,</span> <span class="s2">"n't"</span><span class="p">,</span> <span class="s2">"the"</span><span class="p">,</span> <span class="s2">"easiest"</span><span class="p">,</span> <span class="s2">"text"</span><span class="p">,</span> <span class="s2">"."</span><span class="p">]</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">L</span><span class="p">(</span><span class="n">tok</span><span class="p">([</span><span class="n">inp</span><span class="p">]</span><span class="o">*</span><span class="mi">5</span><span class="p">)),</span> <span class="p">[</span><span class="n">exp</span><span class="p">]</span><span class="o">*</span><span class="mi">5</span><span class="p">)</span>
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<h3 id="TokenizeBatch" class="doc_header"><code>class</code> <code>TokenizeBatch</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L123" class="source_link" style="float:right">[source]</a></h3><blockquote><p><code>TokenizeBatch</code>(<strong><code>tok_func</code></strong>=<em><code>'SpacyTokenizer'</code></em>, <strong><code>rules</code></strong>=<em><code>None</code></em>, <strong><code>post_rules</code></strong>=<em><code>None</code></em>, <strong>**<code>tok_kwargs</code></strong>)</p>
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<p>A wrapper around <code>tok_func</code> to apply <code>rules</code> and tokenize in parallel</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">f</span> <span class="o">=</span> <span class="n">TokenizeBatch</span><span class="p">()</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">f</span><span class="p">([</span><span class="s2">"This isn't a problem"</span><span class="p">]),</span> <span class="p">[[</span><span class="n">BOS</span><span class="p">,</span> <span class="n">TK_MAJ</span><span class="p">,</span> <span class="s1">'this'</span><span class="p">,</span> <span class="s1">'is'</span><span class="p">,</span> <span class="s2">"n't"</span><span class="p">,</span> <span class="s1">'a'</span><span class="p">,</span> <span class="s1">'problem'</span><span class="p">]])</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">TokenizeBatch</span><span class="p">(</span><span class="n">BaseTokenizer</span><span class="p">,</span> <span class="n">rules</span><span class="o">=</span><span class="p">[],</span> <span class="n">split_char</span><span class="o">=</span><span class="s2">"'"</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">f</span><span class="p">([</span><span class="s2">"This isn't a problem"</span><span class="p">]),</span> <span class="p">[[</span><span class="s1">'This▁isn'</span><span class="p">,</span> <span class="s1">'t▁a▁problem'</span><span class="p">]])</span>
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<p>The main function that will be called during one of the processes handling tokenization. It will create an instance of a tokenizer with <code>tok_func</code> and <code>tok_kwargs</code> at init, then iterate through the <code>batch</code> of texts, apply them <code>rules</code> and tokenize them.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">texts</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"this is a text"</span><span class="p">,</span> <span class="s2">"this is another text"</span><span class="p">]</span>
<span class="n">tok</span> <span class="o">=</span> <span class="n">TokenizeBatch</span><span class="p">(</span><span class="n">BaseTokenizer</span><span class="p">,</span> <span class="n">texts</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">([</span><span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tok</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])],[[</span><span class="s1">'this'</span><span class="p">,</span> <span class="s1">'is'</span><span class="p">,</span> <span class="s1">'a'</span><span class="p">,</span> <span class="s1">'text'</span><span class="p">],</span> <span class="p">[</span><span class="s1">'this'</span><span class="p">,</span> <span class="s1">'is'</span><span class="p">,</span> <span class="s1">'another'</span><span class="p">,</span> <span class="s1">'text'</span><span class="p">]])</span>
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<h4 id="tokenize1" class="doc_header"><code>tokenize1</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L134" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>tokenize1</code>(<strong><code>text</code></strong>, <strong><code>tok_func</code></strong>=<em><code>'SpacyTokenizer'</code></em>, <strong><code>rules</code></strong>=<em><code>None</code></em>, <strong><code>post_rules</code></strong>=<em><code>None</code></em>, <strong>**<code>tok_kwargs</code></strong>)</p>
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<p>Tokenize one <a href="/text.html"><code>text</code></a> with an instance of <code>tok_func</code> and some <code>rules</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">(</span><span class="n">tokenize1</span><span class="p">(</span><span class="s2">"This isn't a problem"</span><span class="p">),</span>
<span class="p">[</span><span class="n">BOS</span><span class="p">,</span> <span class="n">TK_MAJ</span><span class="p">,</span> <span class="s1">'this'</span><span class="p">,</span> <span class="s1">'is'</span><span class="p">,</span> <span class="s2">"n't"</span><span class="p">,</span> <span class="s1">'a'</span><span class="p">,</span> <span class="s1">'problem'</span><span class="p">])</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">tokenize1</span><span class="p">(</span><span class="s2">"This isn't a problem"</span><span class="p">,</span> <span class="n">BaseTokenizer</span><span class="p">,</span> <span class="n">rules</span><span class="o">=</span><span class="p">[],</span> <span class="n">split_char</span><span class="o">=</span><span class="s2">"'"</span><span class="p">),</span>
<span class="p">[</span><span class="s1">'This▁isn'</span><span class="p">,</span> <span class="s1">'t▁a▁problem'</span><span class="p">])</span>
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<h4 id="parallel_tokenize" class="doc_header"><code>parallel_tokenize</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L139" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>parallel_tokenize</code>(<strong><code>items</code></strong>, <strong><code>tok_func</code></strong>, <strong><code>rules</code></strong>, <strong><code>as_gen</code></strong>=<em><code>False</code></em>, <strong><code>n_workers</code></strong>=<em><code>64</code></em>, <strong>**<code>tok_kwargs</code></strong>)</p>
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<p>Calls a potential setup on <code>tok_func</code> before launching <a href="/text.core.html#TokenizeBatch"><code>TokenizeBatch</code></a> in parallel</p>
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<h3 id="Tokenize-texts-in-files">Tokenize texts in files<a class="anchor-link" href="#Tokenize-texts-in-files">¶</a></h3>
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<p>Preprocessing function for texts in filenames. Tokenized texts will be saved in a similar fashion in a directory suffixed with <code>_tok</code> in the parent folder of <code>path</code> (override with <code>output_dir</code>).</p>
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<h4 id="tokenize_folder" class="doc_header"><code>tokenize_folder</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L149" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>tokenize_folder</code>(<strong><code>path</code></strong>, <strong><code>extensions</code></strong>=<em><code>None</code></em>, <strong><code>folders</code></strong>=<em><code>None</code></em>, <strong><code>output_dir</code></strong>=<em><code>None</code></em>, <strong><code>n_workers</code></strong>=<em><code>64</code></em>, <strong><code>rules</code></strong>=<em><code>None</code></em>, <strong><code>tok_func</code></strong>=<em><code>'SpacyTokenizer'</code></em>, <strong><code>encoding</code></strong>=<em><code>'utf8'</code></em>, <strong>**<code>tok_kwargs</code></strong>)</p>
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<p>Tokenize text files in <code>path</code> in parallel using <code>n_workers</code></p>
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<h4 id="read_tokenized_file" class="doc_header"><code>read_tokenized_file</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L166" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>read_tokenized_file</code>(<strong><code>f</code></strong>)</p>
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<p>The result will be in <code>output_dir</code> (defaults to a folder in the same parent directory as <code>path</code>, with <code>_tok</code> added to <code>path.name</code>) with the same structure as in <code>path</code>. Tokenized texts for a given file will be in the file having the same name in <code>output_dir</code>. Additionally, a file with a .len suffix contains the number of tokens and the count of all words is stored in <code>output_dir/counter.pkl</code>.</p>
<p><code>extensions</code> will default to <code>['.txt']</code> and all text files in <code>path</code> are treated unless you specify a list of folders in <code>include</code>. <code>tok_func</code> is instantiated in each process with <code>tok_kwargs</code>, and <code>rules</code> (that defaults to <a href="/text.core.html#defaults.text_proc_rules"><code>defaults.text_proc_rules</code></a>) are applied to each text before going in the tokenizer.</p>
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<h3 id="Tokenize-texts-in-a-dataframe">Tokenize texts in a dataframe<a class="anchor-link" href="#Tokenize-texts-in-a-dataframe">¶</a></h3>
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<h4 id="tokenize_df" class="doc_header"><code>tokenize_df</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L177" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>tokenize_df</code>(<strong><code>df</code></strong>, <strong><code>text_cols</code></strong>, <strong><code>n_workers</code></strong>=<em><code>64</code></em>, <strong><code>rules</code></strong>=<em><code>None</code></em>, <strong><code>mark_fields</code></strong>=<em><code>None</code></em>, <strong><code>tok_func</code></strong>=<em><code>'SpacyTokenizer'</code></em>, <strong>**<code>tok_kwargs</code></strong>)</p>
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<p>Tokenize texts in <code>df[text_cols]</code> in parallel using <code>n_workers</code></p>
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<p>This function returns a new dataframe with the same non-text columns, a colum named text that contains the tokenized texts and a column named text_lengths that contains their respective length. It also returns a counter of all words see to quickly build a vocabulary afterward.</p>
<p><code>tok_func</code> is instantiated in each process with <code>tok_kwargs</code>, and <code>rules</code> (that defaults to <a href="/text.core.html#defaults.text_proc_rules"><code>defaults.text_proc_rules</code></a>) are applied to each text before going in the tokenizer. If <code>mark_fields</code> isn't specified, it defaults to <code>False</code> when there is a single text column, <code>True</code> when there are several. In that case, the texts in each of those columns are joined with <code>FLD</code> markes followed by the number of the field.</p>
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<h4 id="tokenize_csv" class="doc_header"><code>tokenize_csv</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L194" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>tokenize_csv</code>(<strong><code>fname</code></strong>, <strong><code>text_cols</code></strong>, <strong><code>outname</code></strong>=<em><code>None</code></em>, <strong><code>n_workers</code></strong>=<em><code>4</code></em>, <strong><code>rules</code></strong>=<em><code>None</code></em>, <strong><code>mark_fields</code></strong>=<em><code>None</code></em>, <strong><code>tok_func</code></strong>=<em><code>'SpacyTokenizer'</code></em>, <strong><code>header</code></strong>=<em><code>'infer'</code></em>, <strong><code>chunksize</code></strong>=<em><code>50000</code></em>, <strong>**<code>tok_kwargs</code></strong>)</p>
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<p>Tokenize texts in the <code>text_cols</code> of the csv <code>fname</code> in parallel using <code>n_workers</code></p>
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<h4 id="load_tokenized_csv" class="doc_header"><code>load_tokenized_csv</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L211" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>load_tokenized_csv</code>(<strong><code>fname</code></strong>)</p>
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<p>Utility function to quickly load a tokenized csv ans the corresponding counter</p>
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<p>The result will be written in a new csv file in <code>outname</code> (defaults to the same as <code>fname</code> with the suffix <code>_tok.csv</code>) and will have the same header as the original file, the same non-text columns, a text and a text_lengths column as described in <a href="/text.core.html#tokenize_df"><code>tokenize_df</code></a>.</p>
<p><code>tok_func</code> is instantiated in each process with <code>tok_kwargs</code>, and <code>rules</code> (that defaults to <a href="/text.core.html#defaults.text_proc_rules"><code>defaults.text_proc_rules</code></a>) are applied to each text before going in the tokenizer. If <code>mark_fields</code> isn't specified, it defaults to <code>False</code> when there is a single text column, <code>True</code> when there are several. In that case, the texts in each of those columns are joined with <code>FLD</code> markes followed by the number of the field.</p>
<p>The csv file is opened with <code>header</code> and optionally with blocks of <code>chunksize</code> at a time. If this argument is passed, each chunk is processed independtly and saved in the output file to save memory usage.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">_prepare_texts</span><span class="p">(</span><span class="n">tmp_d</span><span class="p">):</span>
<span class="s2">"Prepare texts in a folder struct in tmp_d, a csv file and returns a dataframe"</span>
<span class="n">path</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">tmp_d</span><span class="p">)</span><span class="o">/</span><span class="s1">'tmp'</span>
<span class="n">path</span><span class="o">.</span><span class="n">mkdir</span><span class="p">()</span>
<span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">]:</span>
<span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="n">d</span><span class="p">)</span><span class="o">.</span><span class="n">mkdir</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="n">d</span><span class="o">/</span><span class="n">f</span><span class="s1">'text</span><span class="si">{i}</span><span class="s1">.txt'</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span> <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">f</span><span class="s2">"This is an example of text </span><span class="si">{d}</span><span class="s2"> </span><span class="si">{i}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">texts</span> <span class="o">=</span> <span class="p">[</span><span class="n">f</span><span class="s2">"This is an example of text </span><span class="si">{d}</span><span class="s2"> </span><span class="si">{i}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">]]</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'text'</span><span class="p">:</span> <span class="n">texts</span><span class="p">,</span> <span class="s1">'label'</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">15</span><span class="p">))},</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'text'</span><span class="p">,</span> <span class="s1">'label'</span><span class="p">])</span>
<span class="n">csv_fname</span> <span class="o">=</span> <span class="n">tmp_d</span><span class="o">/</span><span class="s1">'input.csv'</span>
<span class="n">df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">csv_fname</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">return</span> <span class="n">path</span><span class="p">,</span><span class="n">df</span><span class="p">,</span><span class="n">csv_fname</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">TemporaryDirectory</span><span class="p">()</span> <span class="k">as</span> <span class="n">tmp_d</span><span class="p">:</span>
<span class="n">path</span><span class="p">,</span><span class="n">df</span><span class="p">,</span><span class="n">csv_fname</span> <span class="o">=</span> <span class="n">_prepare_texts</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="n">tmp_d</span><span class="p">))</span>
<span class="c1">#Tokenize as folders</span>
<span class="n">tokenize_folder</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
<span class="n">outp</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">tmp_d</span><span class="p">)</span><span class="o">/</span><span class="s1">'tmp_tok'</span>
<span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">]:</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">outp</span><span class="o">/</span><span class="n">d</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
<span class="n">test_eq</span><span class="p">((</span><span class="n">p</span><span class="o">/</span><span class="n">f</span><span class="s1">'text</span><span class="si">{i}</span><span class="s1">.txt'</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">(),</span> <span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">([</span>
<span class="n">BOS</span><span class="p">,</span> <span class="n">TK_MAJ</span><span class="p">,</span> <span class="s1">'this'</span><span class="p">,</span> <span class="s1">'is'</span><span class="p">,</span> <span class="s1">'an'</span><span class="p">,</span> <span class="s1">'example'</span><span class="p">,</span> <span class="s1">'of'</span><span class="p">,</span> <span class="s1">'text'</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="p">]))</span>
<span class="n">cnt_a</span> <span class="o">=</span> <span class="p">(</span><span class="n">outp</span><span class="o">/</span><span class="n">fn_counter_pkl</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">cnt_a</span><span class="p">[</span><span class="s1">'this'</span><span class="p">],</span> <span class="mi">15</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">cnt_a</span><span class="p">[</span><span class="s1">'a'</span><span class="p">],</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">cnt_a</span><span class="p">[</span><span class="s1">'0'</span><span class="p">],</span> <span class="mi">3</span><span class="p">)</span>
<span class="c1">#Tokenize as a dataframe</span>
<span class="n">out</span><span class="p">,</span><span class="n">cnt_b</span> <span class="o">=</span> <span class="n">tokenize_df</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">text_cols</span><span class="o">=</span><span class="s1">'text'</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">columns</span><span class="p">),</span> <span class="p">[</span><span class="s1">'label'</span><span class="p">,</span> <span class="s1">'text'</span><span class="p">])</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">out</span><span class="p">[</span><span class="s1">'label'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">'label'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">out</span><span class="p">[</span><span class="s1">'text'</span><span class="p">],</span> <span class="p">[(</span><span class="n">outp</span><span class="o">/</span><span class="n">d</span><span class="o">/</span><span class="n">f</span><span class="s1">'text</span><span class="si">{i}</span><span class="s1">.txt'</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">' '</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">]])</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">cnt_a</span><span class="p">,</span> <span class="n">cnt_b</span><span class="p">)</span>
<span class="c1">#Tokenize as a csv </span>
<span class="n">out_fname</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">tmp_d</span><span class="p">)</span><span class="o">/</span><span class="s1">'output.csv'</span>
<span class="n">tokenize_csv</span><span class="p">(</span><span class="n">csv_fname</span><span class="p">,</span> <span class="n">text_cols</span><span class="o">=</span><span class="s1">'text'</span><span class="p">,</span> <span class="n">outname</span><span class="o">=</span><span class="n">out_fname</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">((</span><span class="n">out</span><span class="p">,</span><span class="n">cnt_b</span><span class="p">),</span> <span class="n">load_tokenized_csv</span><span class="p">(</span><span class="n">out_fname</span><span class="p">))</span>
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<h2 id="Sentencepiece">Sentencepiece<a class="anchor-link" href="#Sentencepiece">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">eu_langs</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"bg"</span><span class="p">,</span> <span class="s2">"cs"</span><span class="p">,</span> <span class="s2">"da"</span><span class="p">,</span> <span class="s2">"de"</span><span class="p">,</span> <span class="s2">"el"</span><span class="p">,</span> <span class="s2">"en"</span><span class="p">,</span> <span class="s2">"es"</span><span class="p">,</span> <span class="s2">"et"</span><span class="p">,</span> <span class="s2">"fi"</span><span class="p">,</span> <span class="s2">"fr"</span><span class="p">,</span> <span class="s2">"ga"</span><span class="p">,</span> <span class="s2">"hr"</span><span class="p">,</span> <span class="s2">"hu"</span><span class="p">,</span>
<span class="s2">"it"</span><span class="p">,</span><span class="s2">"lt"</span><span class="p">,</span><span class="s2">"lv"</span><span class="p">,</span><span class="s2">"mt"</span><span class="p">,</span><span class="s2">"nl"</span><span class="p">,</span><span class="s2">"pl"</span><span class="p">,</span><span class="s2">"pt"</span><span class="p">,</span><span class="s2">"ro"</span><span class="p">,</span><span class="s2">"sk"</span><span class="p">,</span><span class="s2">"sl"</span><span class="p">,</span><span class="s2">"sv"</span><span class="p">]</span> <span class="c1"># all European langs</span>
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<h3 id="SentencePieceTokenizer" class="doc_header"><code>class</code> <code>SentencePieceTokenizer</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/text/core.py#L220" class="source_link" style="float:right">[source]</a></h3><blockquote><p><code>SentencePieceTokenizer</code>(<strong><code>lang</code></strong>=<em><code>'en'</code></em>, <strong><code>special_toks</code></strong>=<em><code>None</code></em>, <strong><code>sp_model</code></strong>=<em><code>None</code></em>, <strong><code>vocab_sz</code></strong>=<em><code>None</code></em>, <strong><code>max_vocab_sz</code></strong>=<em><code>30000</code></em>, <strong><code>model_type</code></strong>=<em><code>'unigram'</code></em>, <strong><code>char_coverage</code></strong>=<em><code>None</code></em>, <strong><code>cache_dir</code></strong>=<em><code>'tmp'</code></em>)</p>
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<p>Spacy tokenizer for <code>lang</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">texts</span> <span class="o">=</span> <span class="p">[</span><span class="n">f</span><span class="s2">"This is an example of text </span><span class="si">{i}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)]</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'text'</span><span class="p">:</span> <span class="n">texts</span><span class="p">,</span> <span class="s1">'label'</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">))},</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'text'</span><span class="p">,</span> <span class="s1">'label'</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">out</span><span class="p">,</span><span class="n">cnt</span> <span class="o">=</span> <span class="n">tokenize_df</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">text_cols</span><span class="o">=</span><span class="s1">'text'</span><span class="p">,</span> <span class="n">tok_func</span><span class="o">=</span><span class="n">SentencePieceTokenizer</span><span class="p">,</span> <span class="n">vocab_sz</span><span class="o">=</span><span class="mi">34</span><span class="p">)</span>
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