In addition, the object exposes several functions : get_dimension # Get the dimension (size) of a lookup vector (hidden layer). So model.wordNgrams will give you the max length of word ngram used LrUpdateRate, t, label, verbose, pretrainedVectors. Maxn, neg, wordNgrams, loss, bucket, thread, This object exposes those training arguments as properties : lr,ĭim, ws, epoch, minCount, minCountLabel, minn, Train_supervised, train_unsupervised and load_modelįunctions return an instance of _FastText class, that we generaly Recall on a test set, we use the test function: def print_results ( N, p, r ): print ( "N \t " str ( N )) print ( bucket # number of buckets thread # number of threads lrUpdateRate # change the rate of updates for the learning rate t # sampling threshold label # label prefix verbose # verbose pretrainedVectors # pretrained word vectors (.vec file) for supervised learning model object To evaluate our model by computing the precision at 1 and the Once the model is trained, we can retrieve the list of words and labels: print ( model. Words that are prefixed by the string _label_ Where is a text file containing a training sentence We can use ain_supervised function like this: import fasttext model = fasttext. In order to train a text classifier using the method described load_model ( "model_filename.bin" )įor more information about word representation usage of fasttext, you save_model ( "model_filename.bin" )Īnd retrieve it later thanks to the function load_model : model = fasttext. You can save your trained model object by calling the function words ) # list of words in dictionary print ( model ) # get the vector of the word 'king' Saving and loading a model object The returned model object represents your learned model, and you can Where data.txt is a training file containing utf-8 encoded text. train_unsupervised ( 'data.txt', model = 'cbow' ) train_unsupervised ( 'data.txt', model = 'skipgram' ) # or, cbow model : model = fasttext. We can use ain_unsupervised function like this: import fasttext # Skipgram model : model = fasttext. In order to learn word vectors, as described $ sudo python setup.py install Usage overview Word representation model Or, to get the latest development version of fasttext, you can installįrom our github repository : $ git clone To install the latest release, you can do : $ pip install fasttext Since it uses C 11 features, it requires a compiler with Table of contentsįastText builds on modern Mac OS and Linuxĭistributions. In this document we present how to use fastText in python. Of word representations and sentence classification. FastText is a library for efficient learning
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