Text Classification

<!doctype html> <html lang="en" class="no-js"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width,initial-scale=1"> <meta name="description" content="Documentation for AutoKeras."> <link rel="canonical" href="http://autokeras.com/tutorial/text_classification/"> <link rel="prev" href="../image_regression/"> <link rel="next" href="../text_regression/"> <link rel="icon" href="/img/favicon.png"> <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.5.14"> <title>Text Classification - AutoKeras</title> <link rel="stylesheet" href="../../assets/stylesheets/main.10ba22f1.min.css"> <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin> <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,300i,400,400i,700,700i%7CRoboto+Mono:400,400i,700,700i&display=fallback"> <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style> <link rel="stylesheet" href="../../stylesheets/extra.css"> <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script> <script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-GTF9QP8DFD"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-GTF9QP8DFD",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-GTF9QP8DFD",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script> <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script> </head> <body dir="ltr"> <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off"> <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off"> <label class="md-overlay" for="__drawer"></label> <div data-md-component="skip"> <a href="#a-simple-example" class="md-skip"> Skip to content </a> </div> <div data-md-component="announce"> </div> <header class="md-header md-header--shadow" data-md-component="header"> <nav class="md-header__inner md-grid" aria-label="Header"> <a href="../.." title="AutoKeras" class="md-header__button md-logo" aria-label="AutoKeras" data-md-component="logo"> <img src="/img/logo_white.svg" alt="logo"> </a> <label class="md-header__button md-icon" for="__drawer"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg> </label> <div class="md-header__title" data-md-component="header-title"> <div class="md-header__ellipsis"> <div class="md-header__topic"> AutoKeras </div> <div class="md-header__topic" data-md-component="header-topic"> Text Classification </div> </div> </div> <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script> <label class="md-header__button md-icon" for="__search"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg> </label> <div class="md-search" data-md-component="search" role="dialog"> <label class="md-search__overlay" for="__search"></label> <div class="md-search__inner" role="search"> <form class="md-search__form" name="search"> <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required> <label class="md-search__icon md-icon" for="__search"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg> </label> <nav class="md-search__options" aria-label="Search"> <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg> </button> </nav> </form> <div class="md-search__output"> <div class="md-search__scrollwrap" data-md-scrollfix> <div class="md-search-result" data-md-component="search-result"> <div class="md-search-result__meta"> Initializing search </div> <ol class="md-search-result__list" role="presentation"></ol> </div> </div> </div> </div> </div> <div class="md-header__source"> <a href="https://github.com/keras-team/autokeras" title="Go to repository" class="md-source" data-md-component="source"> <div class="md-source__icon md-icon"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg> </div> <div class="md-source__repository"> GitHub </div> </a> </div> </nav> </header> <div class="md-container" data-md-component="container"> <main class="md-main" data-md-component="main"> <div class="md-main__inner md-grid"> <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" > <div class="md-sidebar__scrollwrap"> <div class="md-sidebar__inner"> <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0"> <label class="md-nav__title" for="__drawer"> <a href="../.." title="AutoKeras" class="md-nav__button md-logo" aria-label="AutoKeras" data-md-component="logo"> <img src="/img/logo_white.svg" alt="logo"> </a> AutoKeras </label> <div class="md-nav__source"> <a href="https://github.com/keras-team/autokeras" title="Go to repository" class="md-source" data-md-component="source"> <div class="md-source__icon md-icon"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg> </div> <div class="md-source__repository"> GitHub </div> </a> </div> <ul class="md-nav__list" data-md-scrollfix> <li class="md-nav__item"> <a href="../.." class="md-nav__link"> Home </a> </li> <li class="md-nav__item"> <a href="../../install/" class="md-nav__link"> Installation </a> </li> <li class="md-nav__item md-nav__item--active md-nav__item--nested"> <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked> <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0"> Tutorials </label> <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true"> <label class="md-nav__title" for="__nav_3"> Tutorials </label> <ul class="md-nav__list" data-md-scrollfix> <li class="md-nav__item"> <a href="../overview/" class="md-nav__link"> Overview </a> </li> <li class="md-nav__item"> <a href="../image_classification/" class="md-nav__link"> Image Classification </a> </li> <li class="md-nav__item"> <a href="../image_regression/" class="md-nav__link"> Image Regression </a> </li> <li class="md-nav__item md-nav__item--active"> <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc"> <label class="md-nav__link md-nav__link--active" for="__toc"> Text Classification </label> <a href="./" class="md-nav__link md-nav__link--active"> Text Classification </a> <nav class="md-nav md-nav--secondary" aria-label="Table of contents"> <label class="md-nav__title" for="__toc"> Table of contents </label> <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix> <li class="md-nav__item"> <a href="#a-simple-example" class="md-nav__link"> A Simple Example </a> </li> <li class="md-nav__item"> <a href="#validation-data" class="md-nav__link"> Validation Data </a> </li> <li class="md-nav__item"> <a href="#customized-search-space" class="md-nav__link"> Customized Search Space </a> </li> <li class="md-nav__item"> <a href="#data-format" class="md-nav__link"> Data Format </a> </li> <li class="md-nav__item"> <a href="#reference" class="md-nav__link"> Reference </a> </li> </ul> </nav> </li> <li class="md-nav__item"> <a href="../text_regression/" class="md-nav__link"> Text Regression </a> </li> <li class="md-nav__item"> <a href="../multi/" class="md-nav__link"> Multi-Modal and Multi-Task </a> </li> <li class="md-nav__item"> <a href="../customized/" class="md-nav__link"> Customized Model </a> </li> <li class="md-nav__item"> <a href="../export/" class="md-nav__link"> Export Model </a> </li> <li class="md-nav__item"> <a href="../load/" class="md-nav__link"> Load Data from Disk </a> </li> <li class="md-nav__item"> <a href="../faq/" class="md-nav__link"> FAQ </a> </li> </ul> </nav> </li> <li class="md-nav__item md-nav__item--nested"> <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" > <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0"> Extensions </label> <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false"> <label class="md-nav__title" for="__nav_4"> Extensions </label> <ul class="md-nav__list" data-md-scrollfix> <li class="md-nav__item"> <a href="../../extensions/tf_cloud/" class="md-nav__link"> TensorFlow Cloud </a> </li> <li class="md-nav__item"> <a href="../../extensions/trains/" class="md-nav__link"> TRAINS </a> </li> </ul> </nav> </li> <li class="md-nav__item"> <a href="../../docker/" class="md-nav__link"> Docker </a> </li> <li class="md-nav__item"> <a href="../../contributing/" class="md-nav__link"> Contributing Guide </a> </li> <li class="md-nav__item md-nav__item--nested"> <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" > <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0"> Documentation </label> <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false"> <label class="md-nav__title" for="__nav_7"> Documentation </label> <ul class="md-nav__list" data-md-scrollfix> <li class="md-nav__item"> <a href="../../image_classifier/" class="md-nav__link"> ImageClassifier </a> </li> <li class="md-nav__item"> <a href="../../image_regressor/" class="md-nav__link"> ImageRegressor </a> </li> <li class="md-nav__item"> <a href="../../text_classifier/" class="md-nav__link"> TextClassifier </a> </li> <li class="md-nav__item"> <a href="../../text_regressor/" class="md-nav__link"> TextRegressor </a> </li> <li class="md-nav__item"> <a href="../../auto_model/" class="md-nav__link"> AutoModel </a> </li> <li class="md-nav__item"> <a href="../../base/" class="md-nav__link"> Base Class </a> </li> <li class="md-nav__item"> <a href="../../node/" class="md-nav__link"> Node </a> </li> <li class="md-nav__item"> <a href="../../block/" class="md-nav__link"> Block </a> </li> <li class="md-nav__item"> <a href="../../utils/" class="md-nav__link"> Utils </a> </li> </ul> </nav> </li> <li class="md-nav__item"> <a href="../../benchmarks/" class="md-nav__link"> Benchmarks </a> </li> <li class="md-nav__item"> <a href="../../about/" class="md-nav__link"> About </a> </li> </ul> </nav> </div> </div> </div> <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" > <div class="md-sidebar__scrollwrap"> <div class="md-sidebar__inner"> <nav class="md-nav md-nav--secondary" aria-label="Table of contents"> <label class="md-nav__title" for="__toc"> Table of contents </label> <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix> <li class="md-nav__item"> <a href="#a-simple-example" class="md-nav__link"> A Simple Example </a> </li> <li class="md-nav__item"> <a href="#validation-data" class="md-nav__link"> Validation Data </a> </li> <li class="md-nav__item"> <a href="#customized-search-space" class="md-nav__link"> Customized Search Space </a> </li> <li class="md-nav__item"> <a href="#data-format" class="md-nav__link"> Data Format </a> </li> <li class="md-nav__item"> <a href="#reference" class="md-nav__link"> Reference </a> </li> </ul> </nav> </div> </div> </div> <div class="md-content" data-md-component="content"> <article class="md-content__inner md-typeset"> <h1>Text Classification</h1> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7a5 5 0 0 0-5 5 5 5 0 0 0 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1M8 13h8v-2H8v2m9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1 0 1.71-1.39 3.1-3.1 3.1h-4V17h4a5 5 0 0 0 5-5 5 5 0 0 0-5-5Z"/></svg> <a href="https://colab.research.google.com/github/keras-team/autokeras/blob/master/docs/ipynb/text_classification.ipynb">View in Colab</a>    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><path d="M8 0c4.42 0 8 3.58 8 8a8.013 8.013 0 0 1-5.45 7.59c-.4.08-.55-.17-.55-.38 0-.27.01-1.13.01-2.2 0-.75-.25-1.23-.54-1.48 1.78-.2 3.65-.88 3.65-3.95 0-.88-.31-1.59-.82-2.15.08-.2.36-1.02-.08-2.12 0 0-.67-.22-2.2.82-.64-.18-1.32-.27-2-.27-.68 0-1.36.09-2 .27-1.53-1.03-2.2-.82-2.2-.82-.44 1.1-.16 1.92-.08 2.12-.51.56-.82 1.28-.82 2.15 0 3.06 1.86 3.75 3.64 3.95-.23.2-.44.55-.51 1.07-.46.21-1.61.55-2.33-.66-.15-.24-.6-.83-1.23-.82-.67.01-.27.38.01.53.34.19.73.9.82 1.13.16.45.68 1.31 2.69.94 0 .67.01 1.3.01 1.49 0 .21-.15.45-.55.38A7.995 7.995 0 0 1 0 8c0-4.42 3.58-8 8-8Z"/></svg> <a href="https://github.com/keras-team/autokeras/blob/master/docs/py/text_classification.py">GitHub source</a> <div class="highlight"><pre><code>!pip install autokeras </code></pre></div> <div class="highlight"><pre><code>import os import keras import numpy as np import tensorflow as tf from sklearn.datasets import load_files import autokeras as ak </code></pre></div> <h2 id="a-simple-example">A Simple Example</h2> The first step is to prepare your data. Here we use the <a href="https://keras.io/datasets/#imdb-movie-reviews-sentiment-classification">IMDB dataset</a> as an example. <div class="highlight"><pre><code>dataset = keras.utils.get_file( fname="aclImdb.tar.gz", origin="http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz", extract=True, ) # set path to dataset IMDB_DATADIR = os.path.join(os.path.dirname(dataset), "aclImdb") classes = ["pos", "neg"] train_data = load_files( os.path.join(IMDB_DATADIR, "train"), shuffle=True, categories=classes ) test_data = load_files( os.path.join(IMDB_DATADIR, "test"), shuffle=False, categories=classes ) x_train = np.array(train_data.data)[:100] y_train = np.array(train_data.target)[:100] x_test = np.array(test_data.data)[:100] y_test = np.array(test_data.target)[:100] print(x_train.shape) # (25000,) print(y_train.shape) # (25000, 1) print(x_train[0][:50]) # this film was just brilliant casting </code></pre></div> The second step is to run the <a href="/text_classifier">TextClassifier</a>. As a quick demo, we set epochs to 2. You can also leave the epochs unspecified for an adaptive number of epochs. <div class="highlight"><pre><code># Initialize the text classifier. clf = ak.TextClassifier( overwrite=True, max_trials=1 ) # It only tries 1 model as a quick demo. # Feed the text classifier with training data. clf.fit(x_train, y_train, epochs=1, batch_size=2) # Predict with the best model. predicted_y = clf.predict(x_test) # Evaluate the best model with testing data. print(clf.evaluate(x_test, y_test)) </code></pre></div> <h2 id="validation-data">Validation Data</h2> By default, AutoKeras use the last 20% of training data as validation data. As shown in the example below, you can use <code>validation_split</code> to specify the percentage. <div class="highlight"><pre><code>clf.fit( x_train, y_train, # Split the training data and use the last 15% as validation data. validation_split=0.15, epochs=1, batch_size=2, ) </code></pre></div> You can also use your own validation set instead of splitting it from the training data with <code>validation_data</code>. <div class="highlight"><pre><code>split = 5 x_val = x_train[split:] y_val = y_train[split:] x_train = x_train[:split] y_train = y_train[:split] clf.fit( x_train, y_train, epochs=1, # Use your own validation set. validation_data=(x_val, y_val), batch_size=2, ) </code></pre></div> <h2 id="customized-search-space">Customized Search Space</h2> For advanced users, you may customize your search space by using <a href="/auto_model/#automodel-class">AutoModel</a> instead of <a href="/text_classifier">TextClassifier</a>. You can configure the <a href="/block/#textblock-class">TextBlock</a> for some high-level configurations. You can also do not specify these arguments, which would leave the different choices to be tuned automatically. See the following example for detail. <div class="highlight"><pre><code>input_node = ak.TextInput() output_node = ak.TextBlock()(input_node) output_node = ak.ClassificationHead()(output_node) clf = ak.AutoModel( inputs=input_node, outputs=output_node, overwrite=True, max_trials=1 ) clf.fit(x_train, y_train, epochs=1, batch_size=2) </code></pre></div> <h2 id="data-format">Data Format</h2> The AutoKeras TextClassifier is quite flexible for the data format. For the text, the input data should be one-dimensional For the classification labels, AutoKeras accepts both plain labels, i.e. strings or integers, and one-hot encoded encoded labels, i.e. vectors of 0s and 1s. We also support using <a href="https://www.tensorflow.org/api_docs/python/tf/data/Dataset?version=stable">tf.data.Dataset</a> format for the training data. <div class="highlight"><pre><code>train_set = tf.data.Dataset.from_tensor_slices(((x_train,), (y_train,))).batch( 2 ) test_set = tf.data.Dataset.from_tensor_slices(((x_test,), (y_test,))).batch(2) clf = ak.TextClassifier(overwrite=True, max_trials=1) # Feed the tensorflow Dataset to the classifier. clf.fit(train_set.take(2), epochs=1) # Predict with the best model. predicted_y = clf.predict(test_set.take(2)) # Evaluate the best model with testing data. print(clf.evaluate(test_set.take(2))) </code></pre></div> <h2 id="reference">Reference</h2> <a href="/text_classifier">TextClassifier</a>, <a href="/auto_model/#automodel-class">AutoModel</a>, <a href="/block/#convblock-class">ConvBlock</a>, <a href="/node/#textinput-class">TextInput</a>, <a href="/block/#classificationhead-class">ClassificationHead</a>. </article> </div> <script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script> </div> </main> <footer class="md-footer"> <div class="md-footer-meta md-typeset"> <div class="md-footer-meta__inner md-grid"> <div class="md-copyright"> Made with <a href="https://squidfunk.github.io/mkdocs-material/" target="_blank" rel="noopener"> Material for MkDocs </a> </div> </div> </div> </footer> </div> <div class="md-dialog" data-md-component="dialog"> <div class="md-dialog__inner md-typeset"></div> </div> <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script> <script src="../../assets/javascripts/bundle.bd41221c.min.js"></script> <script src="https://unpkg.com/mermaid@8.4.4/dist/mermaid.min.js"></script> </body> </html>

CINXE.COM

Text Classification - AutoKeras