GitLab

* Big cleanup of `glue_convert_examples_to_features`

* Use batch_encode_plus

* Cleaner wrapping of glue_convert_examples_to_features for TF

@lysandrejik

* Cleanup syntax, thanks to @mfuntowicz

* Raise explicit error in case of user error

* improve and add features to benchmark utils

* update benchmark style

* remove output files

* Fix RoBERTa/XLNet Pad Token in run_multiple_choice.py

`convert_examples_to_fes atures` sets `pad_token=0` by default, which is correct for BERT but incorrect for RoBERTa (`pad_token=1`) and XLNet (`pad_token=5`). I think the other arguments to `convert_examples_to_features` are correct, but it might be helpful if someone checked who is more familiar with this part of the codebase.

* Simplifying change to match recent commits

* Resizing embedding matrix after sending it to the optimizer prevents from updating the newly resized matrix.

* Remove space for style matter

* replace heavy t5 models with tiny random models as was done by sshleifer

* fix isort

* Start cleaning examples

* Fixup

* fix conflicts

* add model size argument to summarization

* correct wrong import

* fix isort

* correct imports

* other isort make style

* make style

* Using loaded checkpoint with --do_predict

Without this fix, I'm getting near-random validation performance for a trained model, and the validation performance differs per validation run. I think this happens since the `model` variable isn't set with the loaded checkpoint, so I'm using a randomly initialized model. Looking at the model activations, they differ each time I run evaluation (but they don't with this fix).

* Update checkpoint loading

* Fixing model loading

* Update the NER TF script to remove the softmax and make the pad token label id to -1

* Reformat the quality and style
Co-authored-by: Julien Plu <julien.plu@adevinta.com>

* force bleu

* fix wrong file name

* rename file

* different filenames for each example test

* test files should clean up after themselves

* test files should clean up after themselves

* do not force bleu

* correct typo

* fix isort

* Use tokenizer.num_added_tokens to count number of added special_tokens instead of hardcoded numbers.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* run_ner.py - Do not add a label to the labels_ids if word_tokens is empty.

This can happen when using bert-base-multilingual-cased with an input containing an unique space.
In this case, the tokenizer will output just an empty word_tokens thus leading to an non-consistent behavior
over the labels_ids tokens adding one more tokens than tokens vector.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

T5-small in test


isort

* add translation example

* make style

* adapt docstring

* add gpu device as input for example

* small renaming

* better README

* rebase to master

* change tf to pytorch

* change to pytorch

* small fix

* renaming

* add gpu training possibility

* renaming

* improve README

* incoorporate collins feedback

* better Readme

* better README.md

For more details, see https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate

* make style

* fix conflicts

torch.cuda.empty_cache() was being called from a TF function (even when torch is unavailable)
not sure any replacement is needed if TF OOMs

* solving bug where for small epochs and large gradient_accumulation_steps we never train

* black formatting

* no need to change these files

* Update examples/ner/run_ner.py to use AutoModel

* Fix missing code and apply `make style` command

* ✨ Alter base pl transformer to use automodels

* 🐛 Add batch size env variable to function call

* 💄 Apply black code style from Makefile

* 🚚 Move lightning base out of ner directory

* ✨ Add lightning glue example

* 💄 self

* move _feature_file to base class

* ✨ Move eval logging to custom callback

* 💄 Apply black code style

* 🐛 Add parent to pythonpath, remove copy command

* 🐛 Add missing max_length kwarg

* change do_samples back

* None better default as boolean

* adapt do_sample to True in test example

* make style

* memory benchmark rss

* have both forward pass and line-by-line mem tracing

* cleaned up tracing

* refactored and cleaning up API

* no f-strings yet...

* add GPU mem logging

* fix GPU memory monitoring

* style and quality

* clean up and doc

* update with comments

* Switching to python 3.6+

* fix quality

* Remove unused kwargs
* dont call forward in tests

Bumps [psutil](https://github.com/giampaolo/psutil) from 5.6.3 to 5.6.6.
- [Release notes](https://github.com/giampaolo/psutil/releases)
- [Changelog](https://github.com/giampaolo/psutil/blob/master/HISTORY.rst)
- [Commits](https://github.com/giampaolo/psutil/compare/release-5.6.3...release-5.6.6

)
Signed-off-by: dependabot[bot] <support@github.com>