Can I use a different corpus for fasttext build_vocab than train in Gensim Fasttext?

Question

I am curious to know if there are any implications of using a different source while calling the build_vocab and train of Gensim FastText model. Will this impact the contextual representation of the word embedding?

My intention for doing this is that there is a specific set of words I am interested to get the vector representation for and when calling model.wv.most_similar . I only want words defined in this vocab list to get returned rather than all possible words in the training corpus. I would use the result of this to decide if I want to group those words to be relevant to each other based on similarity threshold.

Following is the code snippet that I am using, appreciate your thoughts if there are any concerns or implication with this approach.

• vocab.txt contains a list of unique words of interest
• corpus.txt contains full conversation text (ie chat messages) where each line represents a paragraph/sentence per chat

A follow up question to this is what values should I set for total_examples & total_words during training in this case?

from gensim.models.fasttext import FastText

model = FastText(min_count=1, vector_size=300,)

corpus_path = f'data/{client}-corpus.txt'
vocab_path = f'data/{client}-vocab.txt'
# Unsure if below counts should be based on the training corpus or vocab
corpus_count = get_lines_count(corpus_path)
total_words = get_words_count(corpus_path)

# build the vocabulary
model.build_vocab(corpus_file=vocab_path)

# train the model
model.train(corpus_file=corpus.corpus_path, epochs=100, 
    total_examples=corpus_count, total_words=total_words,
)

# save the model
model.save(f'models/gensim-fastext-model-{client}')

Answer 1

Incase someone has similar question, I'll paste the reply I got when asking this question in the Gensim Disussion Group for reference:

You can try it, but I wouldn't expect it to work well for most purposes.

The build_vocab() call establishes the known vocabulary of the model, & caches some stats about the corpus.

If you then supply another corpus – & especially one with more words – then:

• You'll want your train() parameters to reflect the actual size of your training corpus. You'll want to provide a true total_examples and total_words count that are accurate for the training-corpus.
• Every word in the training corpus that's not in the know vocabulary is ignored completely, as if it wasn't even there. So you might as well filter your corpus down to just the words-of-interest first, then use that same filtered corpus for both steps. Will the example texts still make sense? Will that be enough data to train meaningful, generalizable word-vectors for just the words-of-interest, alongside other words-of-interest, without the full texts? (You could look at your pref-filtered corpus to get a sense of that.) I'm not sure - it could depend on how severely trimming to just the words-of-interest changed the corpus. In particular, to train high-dimensional dense vectors – as with vector_size=300 – you need a lot of varied data. Such pre-trimming might thin the corpus so much as to make the word-vectors for the words-of-interest far less useful.

You could certainly try it both ways – pre-filtered to just your words-of-interest, or with the full original corpus – and see which works better on downstream evaluations.

More generally, if the concern is training time with the full corpus, there are likely other ways to get an adequate model in an acceptable amount of time.

If using corpus_file mode, you can increase workers to equal the local CPU core count for a nearly-linear speedup from number of cores. (In traditional corpus_iterable mode, max throughput is usually somewhere in the 6-12 workers threads, as long as you ahve that many cores.)

min_count=1 is usually a bad idea for these algorithms: they tend to train faster, in less memory, leaving better vectors for the remaining words when you discard the lowest-frequency words, as the default min_count=5 does. (It's possible FastText can eke a little bit of benefit out of lower-frequency words via their contribution to character-n-gram-training, but I'd only ever lower the default min_count if I could confirm it was actually improving relevant results.

If your corpus is so large that training time is a concern, often a more-aggressive (smaller) sample parameter value not only speeds training (by dropping many redundant high-frequency words), but ofthen improves final word-vector quality for downstream purposes as well (by letting the rarer words have relatively more influence on the model in the absense of the downsampled words).

And again if the corpus is so large that training time is a concern, than epochs=100 is likely overkill. I believe the GoogleNews vectors were trained using only 3 passes – over a gigantic corpus. A sufficiently large & varied corpus, with plenty of examples of all words all throughout, could potentially train in 1 pass – because each word-vector can then get more total training-updates than many epochs with a small corpus. (In general larger epochs values are more often used when the corpus is thin, to eke out something – not on a corpus so large you're considering non-standard shortcuts to speed the steps.)

-- Gordon