ninpnin / probabilistic-word-embeddings Goto Github PK
View Code? Open in Web Editor NEWTrain and evaluate probabilistic word embeddings with Python.
Home Page: https://ninpnin.github.io/probabilistic-word-embeddings/
Train and evaluate probabilistic word embeddings with Python.
Home Page: https://ninpnin.github.io/probabilistic-word-embeddings/
Code review, with the reference implementation from stan.
Currently, the prior is applied with full weight even if a subset of data is used. As this is often the case, there needs to be a way to adjust the strength of the prior to match the amount of data points in the likelihood.
Currently, we now feed in data as a long string that doesn't care about the sentence or document edges. This probably does not have a large effect when we run large-scale experiments. Although, it has two problems/downsides.
The only difference we need to do is to handle arbitrary segments of text rather than one long string.
The best solution is also to just cap the context at the edges, see example below. Although, still treating the whole data as one dataset (with regard to negative samples).
Example:
"The brown dog. \n It jumps over the lazy fix."
CBOW (window size = 1, observations):
p(the| brown)
p(brown|the, dog)
p(dog | brown )
p(it | jumps )
p(jumps | it, over)
p(over| jumps , the)
...
Trying to save output (e) from "largeish" dynamic model with: e.save(file_name) generate error code:
OverflowError: cannot serialize a bytes object larger than 4 GiB
Suggested solution: change line 108 in probabilistic-word-embeddings/probabilistic_word_embeddings/embeddings.py to "pickle.dump(d, f, protocol = 4)"
Python 3.7.6
Ubuntu 20.04.5 LTS
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