FastText using pre-trained word vector for text cl

问题:

I am working on a text classification problem, that is, given some text, I need to assign to it certain given labels.

I have tried using fast-text library by Facebook, which has two utilities of interest to me:

A) Word Vectors with pre-trained models

B) Text Classification utilities

However, it seems that these are completely independent tools as I have been unable to find any tutorials that merge these two utilities.

What I want is to be able to classify some text, by taking advantage of the pre-trained models of the Word-Vectors. Is there any way to do this?

回答1:

FastText's native classification mode depends on you training the word-vectors yourself, using texts with known classes. The word-vectors thus become optimized to be useful for the specific classifications observed during training. So that mode typically wouldn't be used with pre-trained vectors.

If using pre-trained word-vectors, you'd then somehow compose those into a text-vector yourself (for example, by averaging all the words of a text together), then training a separate classifier (such as one of the many options from scikit-learn) using those features.

回答2:

FastText supervised training has -pretrainedVectors argument which can be used like this:

$ ./fasttext supervised -input train.txt -output model -epoch 25 \
       -wordNgrams 2 -dim 300 -loss hs -thread 7 -minCount 1 \
       -lr 1.0 -verbose 2 -pretrainedVectors wiki.ru.vec

Few things to consider:

• Chosen dimension of embeddings must fit the one used in pretrained vectors. E.g. for Wiki word vectors is must be 300. It is set by -dim 300 argument.
• As of mid-February 2018, Python API (v0.8.22) doesn't support training using pretrained vectors (the corresponding parameter is ignored). So you must use CLI (command line interface) version for training. However, a model trained by CLI with pretrained vectors can be loaded by Python API and used for predictions.
• For large number of classes (in my case there were 340 of them) even CLI may break with an exception so you will need to use hierarchical softmax loss function (-loss hs)
• Hierarchical softmax is worse in performance than normal softmax so it can give up all the gain you've got from pretrained embeddings.
• The model trained with pretrained vectors can be several times larger than one trained without.
• In my observation, the model trained with pretrained vectors gets overfitted faster than one trained without