Word2Vec and Doc2Vec support about embetter HOT 3 CLOSED

Ah. I think there's a difference between what you're trying to accomplish and what this library does. The goal of embetter is to make it easy to re-use pre-existing pre-trained embeddings in scikit-learn and to (maybe) fine-tune them.

Your library seems to focus more on training embeddings. Which feels out of scope. My hope is that the finetuning components may compensate for that use-case. I have been toying around with featherbed to train custom embeddings with a "lightweight trick", but I've personally found it hard to train embeddings locally that are better than what other libraries offer pre-trained already. Not just in terms of "cosine-distance-metrics" but also in terms of "inference speed".

As far as I know this is also in certain ways similar to what you want to achieve with TokenWiser.

About that. Part of me regrets creating tokenwiser. It would have been better if I tried the ideas in some experiments before making implementations in a pip-installable package. In hindsight, the ideas didn't work that well and the implementations were pretty slow. You can still download it, use it, but I've stopped maintaining it for a while now. The useful ideas, like the partial pipeline, have moved into separate packages.

Word2Vec and Doc2Vec support

If there's a clear use-cae for add support for these kinds of models, maybe via something like gensim, then this is certainly something we might still discuss.

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Yeah, okay, makes perfect sense. We do need to train the embeddings ourselves as we usually use them to capture implicit semantic relations in the corpora we study. Also we work a lot with Danish, for which there isn't an abundance of great embeddings and it's something the center wants to develop.

I thought tokenwiser was an interesting project, but I definitely understand how it must have been a bit difficult to structure sensibly. I think we will still keep trying to develop some tokenization utilities for model training.

As far as then things in embetter go, I have quite a bit of experience working with gensim's embedding models, if you think Doc2Vec and Word2Vec would be worth having in embetter I can certainly contribute to that. I think it would also be beneficial for our work to have easily employable sklearn compatible components for using our pretrained embeddings.

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This issue was fixed by this PR: #76

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