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View Code? Open in Web Editor NEWCode for the paper "Fine-Grained Entity Typing in Hyperbolic Space"
License: MIT License
Code for the paper "Fine-Grained Entity Typing in Hyperbolic Space"
License: MIT License
Hi,
Thank you for releasing such a nice and easy to use code.
I wanted to ask about procedure to get predictions on a custom dataset. From what I understand infer.py
computes all f-scores on top of predictions using figet.Coach
. I believe this line would allow me get fine grained predictions and I should dump them. Is it correct?
Also for dataset formatting, I should pass the sentences to a NER model and format it in form of left_context
, mention_span
, right_context
right?
Hi,
I think that the enviroment.yml has some errors in it
Hi,
I'm training your model using a different target space.
I'm noticing a very long computation time in the validate-typing-dev-#
phase (15 minutes per epoch when the target space has 63 labeled vectors, 48 minutes when the types are approximately 200).
I know that in this phase the pyFLANN library is used an I think that is the Nearest Neighbor algorithm that slows down this phase.
Since the original dataset presents more than 8k types in the target space I think that you speed up this phase (or maybe I'm missing something in the code, but I didn't touch anything in the validation code) so I am here to ask if there is some method to speed up the training phase.
Thanks
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