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diff --git a/toolkit/components/translations/fasttext/fasttext.js b/toolkit/components/translations/fasttext/fasttext.js
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+++ b/toolkit/components/translations/fasttext/fasttext.js
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+/**
+ * Copyright (c) 2016-present, Facebook, Inc.
+ * All rights reserved.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+let fastTextModule;
+
+const _initFastTextModule = async function (wasmModule) {
+  try {
+    fastTextModule = await loadFastTextModule(wasmModule);
+  } catch(e) {
+    console.error(e);
+  }
+  return true
+}
+
+let postRunFunc = null;
+const addOnPostRun = function (func) {
+  postRunFunc = func;
+};
+
+
+const loadFastText = (wasmModule) => {
+  _initFastTextModule(wasmModule).then((res) => {
+    if (postRunFunc) {
+      postRunFunc();
+    }
+  })
+}
+const thisModule = this;
+const trainFileInWasmFs = 'train.txt';
+const testFileInWasmFs = 'test.txt';
+const modelFileInWasmFs = 'model.bin';
+
+const getFloat32ArrayFromHeap = (len) => {
+  const dataBytes = len * Float32Array.BYTES_PER_ELEMENT;
+  const dataPtr = fastTextModule._malloc(dataBytes);
+  const dataHeap = new Uint8Array(fastTextModule.HEAPU8.buffer,
+    dataPtr,
+    dataBytes);
+  return {
+    'ptr':dataHeap.byteOffset,
+    'size':len,
+    'buffer':dataHeap.buffer
+  };
+};
+
+const heapToFloat32 = (r) => new Float32Array(r.buffer, r.ptr, r.size);
+
+class FastText {
+  constructor(fastTextModule) {
+    this.f = new fastTextModule.FastText();
+  }
+
+  /**
+   * loadModel
+   *
+   * Loads the model file from the specified url, and returns the
+   * corresponding `FastTextModel` object.
+   *
+   * @param {string}     url
+   *     the url of the model file.
+   *
+   * @return {Promise}   promise object that resolves to a `FastTextModel`
+   *
+   */
+  loadModel(url) {
+    const fetchFunc = (thisModule && thisModule.fetch) || fetch;
+
+    const fastTextNative = this.f;
+    return new Promise(function(resolve, reject) {
+      fetchFunc(url).then(response => {
+        return response.arrayBuffer();
+      }).then(bytes => {
+        const byteArray = new Uint8Array(bytes);
+        const FS = fastTextModule.FS;
+        FS.writeFile(modelFileInWasmFs, byteArray);
+      }).then(() =>  {
+        fastTextNative.loadModel(modelFileInWasmFs);
+        resolve(new FastTextModel(fastTextNative));
+      }).catch(error => {
+        reject(error);
+      });
+    });
+  }
+
+  loadModelBinary(buffer) {
+    const fastTextNative = this.f;
+    const byteArray = new Uint8Array(buffer);
+    const FS = fastTextModule.FS;
+    FS.writeFile(modelFileInWasmFs, byteArray);
+    fastTextNative.loadModel(modelFileInWasmFs);
+    return new FastTextModel(fastTextNative);
+  }
+
+  _train(url, modelName, kwargs = {}, callback = null) {
+    const fetchFunc = (thisModule && thisModule.fetch) || fetch;
+    const fastTextNative = this.f;
+
+    return new Promise(function(resolve, reject) {
+      fetchFunc(url).then(response => {
+        return response.arrayBuffer();
+      }).then(bytes => {
+        const byteArray = new Uint8Array(bytes);
+        const FS = fastTextModule.FS;
+        FS.writeFile(trainFileInWasmFs, byteArray);
+      }).then(() =>  {
+        const argsList = ['lr', 'lrUpdateRate', 'dim', 'ws', 'epoch',
+          'minCount', 'minCountLabel', 'neg', 'wordNgrams', 'loss',
+          'model', 'bucket', 'minn', 'maxn', 't', 'label', 'verbose',
+          'pretrainedVectors', 'saveOutput', 'seed', 'qout', 'retrain',
+          'qnorm', 'cutoff', 'dsub', 'qnorm', 'autotuneValidationFile',
+          'autotuneMetric', 'autotunePredictions', 'autotuneDuration',
+          'autotuneModelSize'];
+        const args = new fastTextModule.Args();
+        argsList.forEach(k => {
+          if (k in kwargs) {
+            args[k] = kwargs[k];
+          }
+        });
+        args.model = fastTextModule.ModelName[modelName];
+        args.loss = ('loss' in kwargs) ?
+          fastTextModule.LossName[kwargs['loss']] : 'hs';
+        args.thread = 1;
+        args.input = trainFileInWasmFs;
+
+        fastTextNative.train(args, callback);
+
+        resolve(new FastTextModel(fastTextNative));
+      }).catch(error => {
+        reject(error);
+      });
+    });
+  }
+
+  /**
+   * trainSupervised
+   *
+   * Downloads the input file from the specified url, trains a supervised
+   * model and returns a `FastTextModel` object.
+   *
+   * @param {string}     url
+   *     the url of the input file.
+   *     The input file must must contain at least one label per line. For an
+   *     example consult the example datasets which are part of the fastText
+   *     repository such as the dataset pulled by classification-example.sh.
+   *
+   * @param {dict}       kwargs
+   *     train parameters.
+   *     For example {'lr': 0.5, 'epoch': 5}
+   *
+   * @param {function}   callback
+   *     train callback function
+   *     `callback` function is called regularly from the train loop:
+   *     `callback(progress, loss, wordsPerSec, learningRate, eta)`
+   *
+   * @return {Promise}   promise object that resolves to a `FastTextModel`
+   *
+   */
+  trainSupervised(url, kwargs = {}, callback) {
+    const self = this;
+    return new Promise(function(resolve, reject) {
+      self._train(url, 'supervised', kwargs, callback).then(model => {
+        resolve(model);
+      }).catch(error => {
+        reject(error);
+      });
+    });
+  }
+
+  /**
+   * trainUnsupervised
+   *
+   * Downloads the input file from the specified url, trains an unsupervised
+   * model and returns a `FastTextModel` object.
+   *
+   * @param {string}     url
+   *     the url of the input file.
+   *     The input file must not contain any labels or use the specified label
+   *     prefixunless it is ok for those words to be ignored. For an example
+   *     consult the dataset pulled by the example script word-vector-example.sh
+   *     which is part of the fastText repository.
+   *
+   * @param {string}     modelName
+   *     Model to be used for unsupervised learning. `cbow` or `skipgram`.
+   *
+   * @param {dict}       kwargs
+   *     train parameters.
+   *     For example {'lr': 0.5, 'epoch': 5}
+   *
+   * @param {function}   callback
+   *     train callback function
+   *     `callback` function is called regularly from the train loop:
+   *     `callback(progress, loss, wordsPerSec, learningRate, eta)`
+   *
+   * @return {Promise}   promise object that resolves to a `FastTextModel`
+   *
+   */
+  trainUnsupervised(url, modelName, kwargs = {}, callback) {
+    const self = this;
+    return new Promise(function(resolve, reject) {
+      self._train(url, modelName, kwargs, callback).then(model => {
+        resolve(model);
+      }).catch(error => {
+        reject(error);
+      });
+    });
+  }
+
+}
+
+
+class FastTextModel {
+  /**
+     * `FastTextModel` represents a trained model.
+     *
+     * @constructor
+     *
+     * @param {object}       fastTextNative
+     *     webassembly object that makes the bridge between js and C++
+     */
+  constructor(fastTextNative) {
+    this.f = fastTextNative;
+  }
+
+  /**
+     * isQuant
+     *
+     * @return {bool}   true if the model is quantized
+     *
+     */
+  isQuant() {
+    return this.f.isQuant;
+  }
+
+  /**
+     * getDimension
+     *
+     * @return {int}    the dimension (size) of a lookup vector (hidden layer)
+     *
+     */
+  getDimension() {
+    return this.f.args.dim;
+  }
+
+  /**
+     * getWordVector
+     *
+     * @param {string}          word
+     *
+     * @return {Float32Array}   the vector representation of `word`.
+     *
+     */
+  getWordVector(word) {
+    const b = getFloat32ArrayFromHeap(this.getDimension());
+    this.f.getWordVector(b, word);
+
+    return heapToFloat32(b);
+  }
+
+  /**
+     * getSentenceVector
+     *
+     * @param {string}          text
+     *
+     * @return {Float32Array}   the vector representation of `text`.
+     *
+     */
+  getSentenceVector(text) {
+    if (text.indexOf('\n') != -1) {
+      "sentence vector processes one line at a time (remove '\\n')";
+    }
+    text += '\n';
+    const b = getFloat32ArrayFromHeap(this.getDimension());
+    this.f.getSentenceVector(b, text);
+
+    return heapToFloat32(b);
+  }
+
+  /**
+     * getNearestNeighbors
+     *
+     * returns the nearest `k` neighbors of `word`.
+     *
+     * @param {string}          word
+     * @param {int}             k
+     *
+     * @return {Array.<Pair.<number, string>>}
+     *     words and their corresponding cosine similarities.
+     *
+     */
+  getNearestNeighbors(word, k = 10) {
+    return this.f.getNN(word, k);
+  }
+
+  /**
+     * getAnalogies
+     *
+     * returns the nearest `k` neighbors of the operation
+     * `wordA - wordB + wordC`.
+     *
+     * @param {string}          wordA
+     * @param {string}          wordB
+     * @param {string}          wordC
+     * @param {int}             k
+     *
+     * @return {Array.<Pair.<number, string>>}
+     *     words and their corresponding cosine similarities
+     *
+     */
+  getAnalogies(wordA, wordB, wordC, k) {
+    return this.f.getAnalogies(k, wordA, wordB, wordC);
+  }
+
+  /**
+     * getWordId
+     *
+     * Given a word, get the word id within the dictionary.
+     * Returns -1 if word is not in the dictionary.
+     *
+     * @return {int}    word id
+     *
+     */
+  getWordId(word) {
+    return this.f.getWordId(word);
+  }
+
+  /**
+     * getSubwordId
+     *
+     * Given a subword, return the index (within input matrix) it hashes to.
+     *
+     * @return {int}    subword id
+     *
+     */
+  getSubwordId(subword) {
+    return this.f.getSubwordId(subword);
+  }
+
+  /**
+     * getSubwords
+     *
+     * returns the subwords and their indicies.
+     *
+     * @param {string}          word
+     *
+     * @return {Pair.<Array.<string>, Array.<int>>}
+     *     words and their corresponding indicies
+     *
+     */
+  getSubwords(word) {
+    return this.f.getSubwords(word);
+  }
+
+  /**
+     * getInputVector
+     *
+     * Given an index, get the corresponding vector of the Input Matrix.
+     *
+     * @param {int}             ind
+     *
+     * @return {Float32Array}   the vector of the `ind`'th index
+     *
+     */
+  getInputVector(ind) {
+    const b = getFloat32ArrayFromHeap(this.getDimension());
+    this.f.getInputVector(b, ind);
+
+    return heapToFloat32(b);
+  }
+
+  /**
+     * predict
+     *
+     * Given a string, get a list of labels and a list of corresponding
+     * probabilities. k controls the number of returned labels.
+     *
+     * @param {string}          text
+     * @param {int}             k, the number of predictions to be returned
+     * @param {number}          probability threshold
+     *
+     * @return {Array.<Pair.<number, string>>}
+     *     labels and their probabilities
+     *
+     */
+  predict(text, k = 1, threshold = 0.0) {
+    return this.f.predict(text, k, threshold);
+  }
+
+  /**
+     * getInputMatrix
+     *
+     * Get a reference to the full input matrix of a Model. This only
+     * works if the model is not quantized.
+     *
+     * @return {DenseMatrix}
+     *     densematrix with functions: `rows`, `cols`, `at(i,j)`
+     *
+     * example:
+     *     let inputMatrix = model.getInputMatrix();
+     *     let value = inputMatrix.at(1, 2);
+     */
+  getInputMatrix() {
+    if (this.isQuant()) {
+      throw new Error("Can't get quantized Matrix");
+    }
+    return this.f.getInputMatrix();
+  }
+
+  /**
+     * getOutputMatrix
+     *
+     * Get a reference to the full input matrix of a Model. This only
+     * works if the model is not quantized.
+     *
+     * @return {DenseMatrix}
+     *     densematrix with functions: `rows`, `cols`, `at(i,j)`
+     *
+     * example:
+     *     let outputMatrix = model.getOutputMatrix();
+     *     let value = outputMatrix.at(1, 2);
+     */
+  getOutputMatrix() {
+    if (this.isQuant()) {
+      throw new Error("Can't get quantized Matrix");
+    }
+    return this.f.getOutputMatrix();
+  }
+
+  /**
+     * getWords
+     *
+     * Get the entire list of words of the dictionary including the frequency
+     * of the individual words. This does not include any subwords. For that
+     * please consult the function get_subwords.
+     *
+     * @return {Pair.<Array.<string>, Array.<int>>}
+     *     words and their corresponding frequencies
+     *
+     */
+  getWords() {
+    return this.f.getWords();
+  }
+
+  /**
+     * getLabels
+     *
+     * Get the entire list of labels of the dictionary including the frequency
+     * of the individual labels.
+     *
+     * @return {Pair.<Array.<string>, Array.<int>>}
+     *     labels and their corresponding frequencies
+     *
+     */
+  getLabels() {
+    return this.f.getLabels();
+  }
+
+  /**
+     * getLine
+     *
+     * Split a line of text into words and labels. Labels must start with
+     * the prefix used to create the model (__label__ by default).
+     *
+     * @param {string}          text
+     *
+     * @return {Pair.<Array.<string>, Array.<string>>}
+     *     words and labels
+     *
+     */
+  getLine(text) {
+    return this.f.getLine(text);
+  }
+
+  /**
+     * saveModel
+     *
+     * Saves the model file in web assembly in-memory FS and returns a blob
+     *
+     * @return {Blob}           blob data of the file saved in web assembly FS
+     *
+     */
+  saveModel() {
+    this.f.saveModel(modelFileInWasmFs);
+    const content = fastTextModule.FS.readFile(modelFileInWasmFs,
+      { encoding: 'binary' });
+    return new Blob(
+      [new Uint8Array(content, content.byteOffset, content.length)],
+      { type: ' application/octet-stream' }
+    );
+  }
+
+  /**
+     * test
+     *
+     * Downloads the test file from the specified url, evaluates the supervised
+     * model with it.
+     *
+     * @param {string}          url
+     * @param {int}             k, the number of predictions to be returned
+     * @param {number}          probability threshold
+     *
+     * @return {Promise}   promise object that resolves to a `Meter` object
+     *
+     * example:
+     * model.test("/absolute/url/to/test.txt", 1, 0.0).then((meter) => {
+     *     console.log(meter.precision);
+     *     console.log(meter.recall);
+     *     console.log(meter.f1Score);
+     *     console.log(meter.nexamples());
+     * });
+     *
+     */
+  test(url, k, threshold) {
+    const fetchFunc = (thisModule && thisModule.fetch) || fetch;
+    const fastTextNative = this.f;
+
+    return new Promise(function(resolve, reject) {
+      fetchFunc(url).then(response => {
+        return response.arrayBuffer();
+      }).then(bytes => {
+        const byteArray = new Uint8Array(bytes);
+        const FS = fastTextModule.FS;
+        FS.writeFile(testFileInWasmFs, byteArray);
+      }).then(() =>  {
+        const meter = fastTextNative.test(testFileInWasmFs, k, threshold);
+        resolve(meter);
+      }).catch(error => {
+        reject(error);
+      });
+    });
+  }
+}