FastText is a library for efficient learning of word representations and sentence classification. FastText is provided by Facebook Inc.
FastText.js is a JavaScript library that wraps FastText to run smoothly within node.
This version of FastText.js comes with the following JavaScript APIs
FastText.new(options)
FastText.load
FastText.train
FastText.test
FastText.predict(string)
git clone https://github.com/loretoparisi/fasttext.js.git
cd fasttext.js
npm install
To train the model you must specificy the training set as trainFile and the file where the model must be serialized as serializeTo. All the FastText supervised options are supported. See here for more details about training options. Note that serializeTo does not need to have the file extension in. A bin extension for the quantized model will be automatically added.
var fastText = new FastText({
serializeTo: './band_model',
trainFile: './band_train.txt'
});
fastText.train()
.then(done=> {
console.log("train done.");
})
.catch(error => {
console.error(error);
})To test your model you must specificy the test set file as testFile and the model file to be loaded as loadModel. Optionally you can specificy the precision and recall at k (P@k and R@k) passing the object test: { precisionRecall: k }.
var fastText = new FastText({
loadModel: './band_model.bin',
testFile: './band_test.txt'
});
fastText.test()
.then(done=> {
console.log("test done.");
})
.catch(error => {
console.error(error);
})To inference your model with new data and predict the label you must specify the model file to be loaded as loadModel. You can then call the load method once, and predict(string) to classify a string. Optionally you can specify the k most likely labels to print for each line as predict: { precisionRecall: k }
var sample="Our Twitter run by the band and crew to give you an inside look into our lives on the road. Get #FutureHearts now: http://smarturl.it/futurehearts";
fastText.load()
.then(done => {
return fastText.predict(sample);
})
.then(labels=> {
console.log("TEXT:", sample, "\nPREDICT:",labels );
sample="LBi Software provides precisely engineered, customer-focused #HRTECH solutions. Our flagship solution, LBi HR HelpDesk, is a SaaS #HR Case Management product.";
return fastText.predict(sample);
})
.then(labels=> {
console.log("TEXT:", sample, "\nPREDICT:",labels );
fastText.unload();
})
.catch(error => {
console.error(error);
});A folder examples contains several usage examples of FastText.js.
$ cd examples/
$ node train.js
train [ 'supervised',
'-input',
'/var/folders/_b/szqwdfn979n4fdg7f2j875_r0000gn/T/trainfile.csv',
'-output',
'./data/band_model',
'-dim',
10,
'-lr',
0.1,
'-wordNgrams',
2,
'-minCount',
1,
'-bucket',
10000000,
'-epoch',
5,
'-thread',
4 ]
Read 0M words
Number of words: 517
Number of labels: 2
Progress: 100.0% words/sec/thread: 1853435 lr: 0.000000 loss: 0.681683 eta: 0h0m -14m
exec:fasttext end.
exec:fasttext exit.
train done.
task:fasttext pid:41311 terminated due to receipt of signal:null
$ cd examples/
$ node test.js
test [ 'test',
'./data/band_model.bin',
'/var/folders/_b/szqwdfn979n4fdg7f2j875_r0000gn/T/trainfile.csv',
1 ]
Number of examples: 18
exec:fasttext end.
exec:fasttext exit.
test done.
task:fasttext pid:41321 terminated due to receipt of signal:null
$ cd examples/
$ node predict.js
TEXT: our twitter run by the band and crew to give you an inside look into our lives on the road . get #futurehearts now http //smarturl . it/futurehearts PREDICT: BAND
TEXT: lbi software provides precisely engineered , customer-focused #hrtech solutions . our flagship solution , lbi hr helpdesk , is a saas #hr case management product . PREDICT: ORGANIZATION
To run the model and serve predictions via a simple node http api
$ cd examples/
$ node server.js
model loaded
server is listening on 3000
Try this simple server api passing a text parameter like:
The server api will response in json format
{
response_time: 0.001,
predict: [{
label: "BAND",
score: "0.5"
},
{
label: "ORGANIZATION",
score: "0.498047"
}
]
}
In this example we use the fastText compressed languages model (176 languages) availalble in the full version here
cd examples/
export MODEL=./data/lid.176.ftz
export PORT=9001
node server
and then
that will be correctly detected as KO:
{
"response_time": 0.001,
"predict": [{
"label": "KO",
"score": "1"
},
{
"label": "TR",
"score": "1.95313E-08"
}
]
}The trainFile and testFile are a TSV or CSV file where the fist column is the label, the second column is the text sample. FastText.js will try to normalize the dataset to the FastText format using FastText.prepareDataset method. You do not have to call this method explicitly by the way, FastText.js will do for you. For more info see here.
In this release FastText.js comes with precompiled binaries for linux, macOS and Windows in the lib/bin/ folder. The Windows version is a 64-bit compiled version. It requires the Visual C++ Redistributable for Visual Studio 2015 components. See here for more info about the Windows version.
Precompiled binaries runs FastText natively. A node child_process spawn will fork a new FastText native process tu run at OS speed, manage the state, the errors and the output of the process to the JavaScript API.
For more info about FastText and FastText license see here.