I'm new to DL and NLP, and recently started using a pre-trained fastText embedding model (cc.en.300.bin) through gensim.
I would like to be able to calculate vectors for out-of-vocabulary words myself, by splitting the word to n-grams and looking up the vector for every n-gram.
I could not find a way to export the n-gram vectors that are part of the model. I realize they are hashed, but perhaps there's a way (not necessarily using gensim) to get them?
Any insight will be appreciated!
You can look exactly at how the gensim code creates FastText word-vectors for out-of-vocabulary words by examining its source code for its FastTextKeyedVectors class word_vec() method directly:
(Note that this source code in gensim's develop branch may reflect recent FastText fixes that wouldn't match what your installed package up through gensim version 3.7.1 does; you may want to consult your installed package's local source code, or wait for these fixes to be in an official release.)
Because Python doesn't protect any part of the relevant objects from external access (with things like enforced 'private' designations), you can perform the exact same operations from outside the class.
Note particularly that, in the current code (which matches the behavior of Facebook's original implementation), n-gram vectors will be pulled from the buckets in the hashtable ngram_weights structure whether or not your current n-grams were truly known in the training data or not. In the cases where those n-grams were known and meaningful in the training data, that should help the OOV vector a bit. In the cases where it's getting an arbitrary other vector instead, such randomness shouldn't hurt much.
I recently encountered this issue myself and had to write a script to reduce the model size. The fasttext C code includes a handy function "threshold" to reduce dictionary size, but it's not exposed to the python bindings. After the dictionary reduction you also need to re-build the input matrix, including the ngram buckets that come after the main word vectors. After saving the model in this way, all word vectors will be generated from the subword information alone (except for the dictionary words that remain)
for word similarity search, output_ and model_ are not used. To save more memory you can also just comment out the part that writes output_ in saveModel
note that the ngram entries by themselves are about 2Gb in the english pretrained model, so that's about the smallest you can make the model even if all dictionary words are removed.
/* note: some dict_ members are public for easier access */
void FastText::quantize(const Args& qargs) {
/*if (args_->model != model_name::sup) {
throw std::invalid_argument(
"For now we only support quantization of supervised models");
}*/
args_->input = qargs.input;
args_->qout = qargs.qout;
args_->output = qargs.output;
std::shared_ptr<DenseMatrix> input =
std::dynamic_pointer_cast<DenseMatrix>(input_);
std::shared_ptr<DenseMatrix> output =
std::dynamic_pointer_cast<DenseMatrix>(output_);
bool normalizeGradient = (args_->model == model_name::sup);
if (qargs.cutoff > 0 && qargs.cutoff < input->size(0)) {
/*auto idx = selectEmbeddings(qargs.cutoff);
dict_->prune(idx);*/
int32_t rows = dict_->size_+args_->bucket;
dict_->threshold(2000, 2000);
std::cerr << "words: " << dict_->size_ << std::endl;
std::cerr << "rows: " << rows << std::endl;
/*std::shared_ptr<DenseMatrix> ninput =
std::make_shared<DenseMatrix>(idx.size(), args_->dim);*/
int32_t new_rows = dict_->size_+args_->bucket;
std::shared_ptr<DenseMatrix> ninput = std::make_shared<DenseMatrix>(dict_->size_+args_->bucket, args_->dim);
for (auto i = 0; i < dict_->size_; i++) {
for (auto j = 0; j < args_->dim; j++) {
int32_t index = dict_->getId(dict_->words_[i].word);
ninput->at(i, j) = input->at(index, j);
}
}
int32_t offset = rows-new_rows;
for (auto i = dict_->size_; i < new_rows; i++) {
for (auto j = 0; j < args_->dim; j++) {
ninput->at(i, j) = input->at(i+offset, j);
}
}
/*input = ninput;*/
input_ = ninput;
if (qargs.retrain) {
args_->epoch = qargs.epoch;
args_->lr = qargs.lr;
args_->thread = qargs.thread;
args_->verbose = qargs.verbose;
auto loss = createLoss(output_);
model_ = std::make_shared<Model>(input, output, loss, normalizeGradient);
startThreads();
}
}
/*input_ = std::make_shared<QuantMatrix>(
std::move(*(input.get())), qargs.dsub, qargs.qnorm);*/
/*if (args_->qout) {
output_ = std::make_shared<QuantMatrix>(
std::move(*(output.get())), 2, qargs.qnorm);
}
*/
/*quant_ = true;*/
auto loss = createLoss(output_);
model_ = std::make_shared<Model>(input_, output_, loss, normalizeGradient);
}
