Hi, I am interested in using the DeBERTa model that was recently implemented here and incorporating it into FARM so that it can also be used in open-domain QA settings through Haystack.

Just wondering why there's only a Slow Tokenizer implemented for DeBERTa and wondering if there are plans to create the Fast Tokeni

A user reported a documentation issue on the mailing list: https://groups.google.com/g/gensim/c/8nobtm9tu-g.

The report shows two problems:

1. Something changed with wmdistance between 3.8 and 4.0 that is not properly captured in the Migration notes.

2. The [WMD tutorial](https://radimrehurek.com/gensim_4

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?

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)))