word2vec-api

Simple web service providing a word embedding API. The methods are based on Gensim Word2Vec implementation. Models are passed as parameters and must be in the Word2Vec text or binary format. Updated to run on Python 3.

• Install Dependencies

pip install -r requirements.txt

• Launching the service

python word2vec-api --model path/to/the/model [--host host --port 1234]

or

python word2vec-api.py --model /path/to/GoogleNews-vectors-negative300.bin --binary BINARY --path /word2vec --host 0.0.0.0 --port 5000

• Example calls

curl http://127.0.0.1:5000/word2vec/n_similarity?ws1=Sushi&ws1=Shop&ws2=Japanese&ws2=Restaurant
curl http://127.0.0.1:5000/word2vec/similarity?w1=Sushi&w2=Japanese
curl http://127.0.0.1:5000/word2vec/most_similar?positive=indian&positive=food[&negative=][&topn=]
curl http://127.0.0.1:5000/word2vec/model?word=restaurant
curl http://127.0.0.1:5000/word2vec/model_word_set

Note: The "model" method returns a base64 encoding of the vector. "model_word_set" returns a base64 encoded pickle of the model's vocabulary.

Where to get a pretrained model

In case you do not have domain specific data to train, it can be convenient to use a pretrained model. Please feel free to submit additions to this list through a pull request.