Let's use this Issue to track performance issues and enhancement requests, so it's easier to prioritize the work.

This is for pytorch transformers

Also I will label it as a Good Difficult Issue in case someone is ready for a challenging but rewarding experience of figuring things out. If you do want to take the challenge comment in the corresponding Issue/PR that resonates with you s

Change tensor.data to tensor.detach() due to
pytorch/pytorch#6990 (comment)
tensor.detach() is more robust than tensor.data.

(triggered by SO question: https://stackoverflow.com/questions/67944732/using-my-own-stopword-list-with-gensim-corpora-textcorpus-textcorpus/67951592#67951592)

Gensim has two remove_stopwords() functions with similar, but slightly-different behavior that risks confusing users.

gensim.parsing.preprocessing.remove_stopwords takes a space-delimited string, and always consults the current

Is your feature request related to a problem? Please describe.
I typically used compressed datasets (e.g. gzipped) to save disk space. This works fine with AllenNLP during training because I can write my dataset reader to load the compressed data. However, the predict command opens the file and reads lines for the Predictor. This fails when it tries to load data from my compressed files.

Hi
Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq

Hello spoooopyyy hackers 🎃

This is a Hacktoberfest only issue! 👻

This is also data-sciency!

The Problem

Our English dictionary contains words that aren't English, and does not contain common English words.

Examples of non-common words in the dictionary:

"hlithskjalf",
  "hlorrithi",
  "hlqn",
  "hm",
  "hny",
  "ho",
  "hoactzin",
  "hoactzine