I downloaded word embedding from this link. I want to load it in Gensim to do some work but I am not able to load it. I have found many resources and none of it is working. I am using Gensim version 4.1.
I have tried
gensim.models.fasttext.load_facebook_model('/home/admin1/embeddings/crawl-300d-2M.vec')
gensim.models.fasttext.load_facebook_vectors('/home/admin1/embeddings/crawl-300d-2M.vec')
and it is showing me
NotImplementedError: Supervised fastText models are not supported
I went to try to load it using using FastText.load('/home/admin1/embeddings/crawl-300d-2M.vec',) but then it showed UnpicklingError: could not find MARK.
Also, using
Per the NotImplementedError, those are the one kind of full Facebook FastText model, -supervised mode, that Gensim does not support.
So sadly, the answer to "How do you load these?" is "you don't".
The .vec files contain just the full-word vectors in a plain-text format – no subword info for synthesizing OOV vectors, or supervised-classification output features. Those can be loaded into a KeyedVectors model:
kv_model = KeyedVectors.load_word2vec_format('crawl-300d-2M.vec')
Related
I want to fetch vector representation of words.
I tried to use GENSIM api but got the same error as here (for Python 3.6):
ValueError when downloading gensim data set
What is the best way to get the vector out of the pre-trained model?
You can download the compressed vectors directly form the Google link on the page:
https://code.google.com/archive/p/word2vec/
(Search for GoogleNews-vectors to find the link about 2/3 through the page.)
Take note of the local file path where you downloaded the file.
Then load the set of vecors as a Gensim KeyedVectors model:
from gensim.models import KeyedVectors
goog_model = KeyedVectors.load_word2vec_format('/WHERE/YOU/DOWNLOADED/GoogleNews-vectors-negative300.bin.gz', binary=True)
Is Google's pretrained word2vec model CBO or skipgram.
We load pretrained model by:
from gensim.models.keyedvectors as word2vec
model= word2vec.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz')
How can we specifically load pretrained CBOW or skipgram model ?
The GoogleNews word-vectors were trained by Google, using a proprietary corpus, but they're never explicitly described all the training-parameters used. (It's not encoded in the file.)
It's been asked a number of times on the Google Group devoted to the word2vec-toolkit code, without a definitive answer. For example, there's a response from word2vec author Mikolov that he doesn't remember the training parameters. Elsewhere, another poster thinks one of the word2vec papers implies skip-gram was used – but as that passage doesn't precisely match other aspects (like vocabulary-size) of the released GoogleNews vectors, I wouldn't be completely confident of that.
As Google hasn't been clear, and in any case hasn't released alternate versions based on different training modes, if you want to run any tests or make any conclusions about the different modes, you'll have to use other vector-sets, or train your own vectors in varying ways.
Late to the party, but Mikolov describes the hyperparameters here. The Google News pretrained vectors were trained using CBOW. I believe that's the only option for you to load; there is no pretrained skip-gram version available.
I've recently started experimenting with pre-trained word embeddings to enhance the performance of my LSTM model on a NLP task. In this case, I looked into Google's Word2Vec. Based on online tutorials, I first downloaded Word2Vec with wget https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz and used python's gensim package to query the embeddings, using the following code.
from gensim.models import KeyedVectors
if __name__ == "__main__":
model = KeyedVectors.load_word2vec_format("./data/word2vec/GoogleNews-vectors-negative300.bin", binary=True)
print(model["bosnia"])
However, after noticing that many common words weren't found in the model, I started to wonder if something was awry. I tried searching for bosnia in the embedding repo, as shown above, but it wasn't found. So, I went on the TensorFlow embedding projector, loaded the Word2Vec model, and searched for bosnia - it was there.
So, my question is: why is this happening? Was the version of Word2Vec I downloaded not complete? Or is gensim unable to load all words into memory and therefore omitting some?
You should check the length of the downloaded file(s), to ensure it's as expected (in case it was truncated or incompletely downloaded).
You should double-check that you're using the same file in both places, and also checking the exact same token (eg 'bosnia' vs 'Bosnia') via both paths. (None of the 5 options in the https://projector.tensorflow.org/ drop-down correspond to the GoogleNews 300-d, 3-million-token dataset, and the load button doesn't appear to support word2vec .bin files, so I'm not sure how that could be used to cross-check what's in that file.)
(There aren't any known bugs in gensim's load_word2vec_format() that would explain it missing vectors that are actually present.)
Spacy has great parsing capacities and it's API is very intuitive for the most part. Is there any way from the Spacy API to fine tune its word embedding models? In particular, I would like to keep Spacy's tokens and give them a vector when possible.
The only thing I've come across for now is to train the embeddings using gensim (but then I wouldn't know how to load the embeddings from spacy to gensim) and then load then back to spacy, as in: https://www.shanelynn.ie/word-embeddings-in-python-with-spacy-and-gensim/. This doesn't help for the first part: training on spacy tokens.
Any help appreciated.
From the spacy documentation:
If you need to train a word2vec model, we recommend the implementation
in the Python library Gensim.
Besides gensim you can also use other implementations like FastText . The easiest way to use the custom vectors from spacy, is to create a model using the init-model command-line utility, like this:
wget https://s3-us-west-1.amazonaws.com/fasttext-vectors/word-vectors-v2/cc.la.300.vec.gz
python -m spacy init-model en model --vectors-loc cc.la.300.vec.gz
then simply load your model as usual: nlp = spacy.load('model'). There is a detailed documentation in the spacy website.
I am training word vectors on particular text corpus using fast text.
Fasttext provides all the necessary mechanics and options for training word vectors and when looked with tsne, the vectors are amazing. I notice gensim has a wrapper for fasttext which is good for accessing vectors.
for my task, I have many text corpuses. I need to use the above trained vectors again with new corpus and use the trained vectors again on new discovered corpuses. fasttext doesnot provide this function. I donot see any package that achieves this or may be I am lost. I see in google forum gensim provides intersect_word2vec_format, but cannot understand or find usage tutorial for this. There is another question open similar to this with no answer.
So apart from gensim, is there any other way to train the models like above.