Joseph Turian

I would like this feature too. Consider adding a SECOND method instead of cluttering `extract_keywords`. The reason for this is because if you are doing keyphrase extraction over many documents,...

Or, can you propose a workaround? e.g. a posthoc embedding extraction approach?

It seems similar but don't completely understand what https://github.com/MaartenGr/KeyBERT/issues/127 is asking for. What I would propose is a low-level method `extract_keywords_full_return` which returns everything, and then `extract_keywords` drops the other...

Rather, this: ``` kw_model = KeyBERT() keywords, embeddings = kw_model .extract_keywords_full_return() ```

Since I am an experienced ML practitioner, I would manipulate the embeddings directly for my downstream usecase.

Here is an example: https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/01-mnist-hello-world.ipynb In my code (not the colab above, but a similar style), I don't OOM when I create the model. I OOM when I run ```...

@Stonesjtu if I have an nn.Module that contains other nn.Modules (which in turn contain other nn.Modules), do I add @profile decorator to all nn.Modules to see what is happening? Thank...

I need this too

@apls777 I have the same issue and it's urgent :( This is my spotty.yaml, could you show a simple working GCP spotty yaml? ``` project: name: spotty-heareval syncFilters: - exclude:...

thank you!