How to load a pre-trained Word2vec MODEL File and reuse it?
Question:
I want to use a pre-trained word2vec model, but I don’t know how to load it in python.
This file is a MODEL file (703 MB).
It can be downloaded here:
http://devmount.github.io/GermanWordEmbeddings/
Answers:
just for loading
import gensim
# Load pre-trained Word2Vec model.
model = gensim.models.Word2Vec.load("modelName.model")
now you can train the model as usual. also, if you want to be able to save it and retrain it multiple times, here’s what you should do
model.train(//insert proper parameters here//)
"""
If you don't plan to train the model any further, calling
init_sims will make the model much more memory-efficient
If `replace` is set, forget the original vectors and only keep the normalized
ones = saves lots of memory!
replace=True if you want to reuse the model
"""
model.init_sims(replace=True)
# save the model for later use
# for loading, call Word2Vec.load()
model.save("modelName.model")
Use KeyedVectors to load the pre-trained model.
from gensim.models import KeyedVectors
from gensim import models
word2vec_path = 'path/GoogleNews-vectors-negative300.bin.gz'
w2v_model = models.KeyedVectors.load_word2vec_format(word2vec_path, binary=True)
I used the same model in my code and since I couldn’t load it, I asked the author about it. His answer was that the model has to be loaded in binary format:
gensim.models.KeyedVectors.load_word2vec_format(w2v_path, binary=True)
This worked for me, and I think it should work for you, too.
I met the same issue and I downloaded GoogleNews-vectors-negative300 from Kaggle. I saved and extracted the file in my descktop. Then I implemented this code in python and it worked well:
model = KeyedVectors.load_word2vec_format=(r'C:/Users/juana/descktop/archive/GoogleNews-vectors-negative300.bin')
I want to use a pre-trained word2vec model, but I don’t know how to load it in python.
This file is a MODEL file (703 MB).
It can be downloaded here:
http://devmount.github.io/GermanWordEmbeddings/
just for loading
import gensim
# Load pre-trained Word2Vec model.
model = gensim.models.Word2Vec.load("modelName.model")
now you can train the model as usual. also, if you want to be able to save it and retrain it multiple times, here’s what you should do
model.train(//insert proper parameters here//)
"""
If you don't plan to train the model any further, calling
init_sims will make the model much more memory-efficient
If `replace` is set, forget the original vectors and only keep the normalized
ones = saves lots of memory!
replace=True if you want to reuse the model
"""
model.init_sims(replace=True)
# save the model for later use
# for loading, call Word2Vec.load()
model.save("modelName.model")
Use KeyedVectors to load the pre-trained model.
from gensim.models import KeyedVectors
from gensim import models
word2vec_path = 'path/GoogleNews-vectors-negative300.bin.gz'
w2v_model = models.KeyedVectors.load_word2vec_format(word2vec_path, binary=True)
I used the same model in my code and since I couldn’t load it, I asked the author about it. His answer was that the model has to be loaded in binary format:
gensim.models.KeyedVectors.load_word2vec_format(w2v_path, binary=True)
This worked for me, and I think it should work for you, too.
I met the same issue and I downloaded GoogleNews-vectors-negative300 from Kaggle. I saved and extracted the file in my descktop. Then I implemented this code in python and it worked well:
model = KeyedVectors.load_word2vec_format=(r'C:/Users/juana/descktop/archive/GoogleNews-vectors-negative300.bin')