Recently HF trainer was extended to support full fp16 eval via --fp16_full_eval. I'd have expected it to be either equal or faster than eval with fp32 model, but surprisingly I have noticed a 25% slowdown when using it.

This may or may not impact deepspeed as well, which also runs eval in fp16, but we can't compare it to a baseline, since it only runs fp16.

I wonder if someone would like t

Not a high-priority at all, but it'd be more sensible for such a tutorial/testing utility corpus to be implemented elsewhere - maybe under /test/ or some other data- or doc- related module – rather than in gensim.models.word2vec.

Originally posted by @gojomo in RaRe-Technologies/gensim#2939 (comment)

While setting train_parameters to False very often we also may consider disabling dropout/batchnorm, in other words, to run the pretrained model in eval mode.
We've done a little modification to PretrainedTransformerEmbedder that allows providing whether the token embedder should be forced to eval mode during the training phase.

Do you this feature might be handy? Should I open a PR?

This happens after a map operation when num_proc is set to >1. I tested this by cleaning up the json before running the map op on the dataset so it's unlikely it's coming from an earlier concatenation.

Example result:

"citation": "@ONLINE {wikidump,\n    author = {Wikimedia Foundation},\n    title  = {Wikimedia Downloads},\n    url    = {https://dumps.wikimedia.org}\n}\n\n@ONLINE 

Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))