vocab | Vocab is a strongly typed internationalization framework for React | Translation library

As https://github.com/pytorch/text/issues/1445 mentioned, you should change "Vocab" to "vocab". I think they miss-type the legacy-to-new notebook.

correct code:

Remove the .vocab here in model_glove.vocab, this is not supported in the current version of gensim any more: Edit: also needs split() to iterate over words and not characters here.

spacy version: 3.1.4 does not have the feature from_glove.

I was able to use nlp.vocab.vectors.from_glove() in spacy version: 2.2.4.

If you want, you can change your spacy version by using:

!pip install spacy==2.2.4 on your Jupyter cell.

I tried your code and I had a few minor issues wgich I fixed on the code below.

Note that SaveTest.nlp is a binary file with your doc info and
SaveTest.voc is a folder with all the spacy model vocab information (vectors, strings among other).

Changes I made:

1. Import Doc class from spacy.tokens
2. Import Vocab class from spacy.vocab
3. Download en_core_web_md model using the following command:

The solution / issue isn't fundamentally different from the question linked to, there's no facility for repeating multi-token patterns in a match like that. You can use a for loop to build multiple patterns to capture what you want.

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

Import GensimWord2VecVectorizer in your Flask Web app python file.

The spaCy Vocab is mainly an internal implementation detail to interface with a memory-efficient method of storing strings. It is definitely not a list of "real words" or any other thing that you are likely to find useful.

The main thing a Vocab stores by default is strings that are used internally, such as POS and dependency labels. In pipelines with vectors, words in the vectors are also included. You can read more about the implementation details here.

All words an nlp object has seen need storage for their strings, and so will be present in the Vocab. That's what you're seeing with your nonsense string in the example above.

Please try this: