Created
February 19, 2015 11:32
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| import gensim | |
| import codecs | |
| from gensim.models import Word2Vec | |
| import json | |
| def export_to_file(path_to_model, output_file): | |
| output = codecs.open(output_file, 'w' , 'utf-8') | |
| model = Word2Vec.load_word2vec_format(path_to_model, binary=True) | |
| vocab = model.vocab | |
| for mid in vocab: | |
| #print(model[mid]) | |
| print(mid) | |
| vector = list() | |
| for dimension in model[mid]: | |
| vector.append(str(dimension)) | |
| #line = { "mid": mid, "vector": vector } | |
| vector_str = ",".join(vector) | |
| line = mid + "\t" + vector_str | |
| #line = json.dumps(line) | |
| output.write(line + "\n") | |
| output.close() |
Note, gensim.models.Word2Vec.load_word2vec_format has been deprecated. Switching "Word2Vec" to be "KeyedVectors" works.
(I got here from https://github.com/sriniiyer/nl2sql which references this code).
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Works great, thanks! With GoogleNews-vectors-negative300.bin.gz it requires around 5.1 GB of RAM, the output file is 9.5 GB, and it takes ~30 minutes on a decent CPU.