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<h3 id="text-preprocessing">Text Preprocessing</h3>
<p><span style="float:right;"><a href="https://github.com/keras-team/keras/blob/master/keras/preprocessing/text.py#L139">[source]</a></span></p>
<h3 id="tokenizer">Tokenizer</h3>
<pre><code class="python">keras.preprocessing.text.Tokenizer(num_words=None, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', lower=True, split=' ', char_level=False, oov_token=None, document_count=0)
</code></pre>
<p>Text tokenization utility class.</p>
<p>This class allows to vectorize a text corpus, by turning each
text into either a sequence of integers (each integer being the index
of a token in a dictionary) or into a vector where the coefficient
for each token could be binary, based on word count, based on tf-idf...</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>num_words</strong>: the maximum number of words to keep, based
on word frequency. Only the most common <code>num_words-1</code> words will
be kept.</li>
<li><strong>filters</strong>: a string where each element is a character that will be
filtered from the texts. The default is all punctuation, plus
tabs and line breaks, minus the <code>'</code> character.</li>
<li><strong>lower</strong>: boolean. Whether to convert the texts to lowercase.</li>
<li><strong>split</strong>: str. Separator for word splitting.</li>
<li><strong>char_level</strong>: if True, every character will be treated as a token.</li>
<li><strong>oov_token</strong>: if given, it will be added to word_index and used to
replace out-of-vocabulary words during text_to_sequence calls</li>
</ul>
<p>By default, all punctuation is removed, turning the texts into
space-separated sequences of words
(words maybe include the <code>'</code> character). These sequences are then
split into lists of tokens. They will then be indexed or vectorized.</p>
<p><code>0</code> is a reserved index that won't be assigned to any word.</p>
<hr />
<h3 id="hashing_trick">hashing_trick</h3>
<pre><code class="python">keras.preprocessing.text.hashing_trick(text, n, hash_function=None, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', lower=True, split=' ')
</code></pre>
<p>Converts a text to a sequence of indexes in a fixed-size hashing space.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>text</strong>: Input text (string).</li>
<li><strong>n</strong>: Dimension of the hashing space.</li>
<li><strong>hash_function</strong>: defaults to python <code>hash</code> function, can be 'md5' or
any function that takes in input a string and returns a int.
Note that 'hash' is not a stable hashing function, so
it is not consistent across different runs, while 'md5'
is a stable hashing function.</li>
<li><strong>filters</strong>: list (or concatenation) of characters to filter out, such as
punctuation. Default: <code>!"#$%&()*+,-./:;<=>?@[\]^_`{|}~\t\n</code>,
includes basic punctuation, tabs, and newlines.</li>
<li><strong>lower</strong>: boolean. Whether to set the text to lowercase.</li>
<li><strong>split</strong>: str. Separator for word splitting.</li>
</ul>
<p><strong>Returns</strong></p>
<p>A list of integer word indices (unicity non-guaranteed).</p>
<p><code>0</code> is a reserved index that won't be assigned to any word.</p>
<p>Two or more words may be assigned to the same index, due to possible
collisions by the hashing function.
The <a href="https://en.wikipedia.org/wiki/Birthday_problem#Probability_table">probability</a>
of a collision is in relation to the dimension of the hashing space and
the number of distinct objects.</p>
<hr />
<h3 id="one_hot">one_hot</h3>
<pre><code class="python">keras.preprocessing.text.one_hot(text, n, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', lower=True, split=' ')
</code></pre>
<p>One-hot encodes a text into a list of word indexes of size n.</p>
<p>This is a wrapper to the <code>hashing_trick</code> function using <code>hash</code> as the
hashing function; unicity of word to index mapping non-guaranteed.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>text</strong>: Input text (string).</li>
<li><strong>n</strong>: int. Size of vocabulary.</li>
<li><strong>filters</strong>: list (or concatenation) of characters to filter out, such as
punctuation. Default: <code>!"#$%&()*+,-./:;<=>?@[\]^_`{|}~\t\n</code>,
includes basic punctuation, tabs, and newlines.</li>
<li><strong>lower</strong>: boolean. Whether to set the text to lowercase.</li>
<li><strong>split</strong>: str. Separator for word splitting.</li>
</ul>
<p><strong>Returns</strong></p>
<p>List of integers in [1, n]. Each integer encodes a word
(unicity non-guaranteed).</p>
<hr />
<h3 id="text_to_word_sequence">text_to_word_sequence</h3>
<pre><code class="python">keras.preprocessing.text.text_to_word_sequence(text, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', lower=True, split=' ')
</code></pre>
<p>Converts a text to a sequence of words (or tokens).</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>text</strong>: Input text (string).</li>
<li><strong>filters</strong>: list (or concatenation) of characters to filter out, such as
punctuation. Default: <code>!"#$%&()*+,-./:;<=>?@[\]^_`{|}~\t\n</code>,
includes basic punctuation, tabs, and newlines.</li>
<li><strong>lower</strong>: boolean. Whether to convert the input to lowercase.</li>
<li><strong>split</strong>: str. Separator for word splitting.</li>
</ul>
<p><strong>Returns</strong></p>
<p>A list of words (or tokens).</p>
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