GitLab

Added citation and paper links.

* Add xlm-r QA model card

* Add tags

* Add license info to german bert

* improve if else statement random seeds

* Apply suggestions from code review

* Update src/transformers/modeling_reformer.py

* Adding docs for how to load encoder_decoder pretrained model with individual config objects

* Adding docs for loading encoder_decoder config from pretrained folder

* Fixing  W293 blank line contains whitespace

* Update src/transformers/modeling_encoder_decoder.py

* Update src/transformers/modeling_encoder_decoder.py

* Update src/transformers/modeling_encoder_decoder.py

* Apply suggestions from code review

model file should only show examples for how to load save model

* Update src/transformers/configuration_encoder_decoder.py

* Update src/transformers/configuration_encoder_decoder.py

* fix space
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* improve unit tests

this is a sample of one test according to the request in https://github.com/huggingface/transformers/issues/5973
before I apply it to the rest

* batch 1

* batch 2

* batch 3

* batch 4

* batch 5

* style

* non-tf template

* last deletion of check_loss_output

* Make the order of additional special tokens deterministic regardless of hash seeds

* Fix

* Upgrade pip when doing CI

* Don't forget Github CI

* Document better MT command

* improve multigpu command

* Remove out-dated BERT tips

* Update modeling_outputs.py

* Update bert.rst

* Update bert.rst

* Init work on pipelines doc

* Work in progress

* Work in progress

* Doc pipelines

* Rm unwanted default

* Apply suggestions from code review

Lysandre comments
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Adds train_batch_size, eval_batch_size, and n_gpu to to_sanitized_dict() output

* Update wandb config logging to use to_sanitized_dict

* removed n_gpu from sanitized dict

* fix quality check errors

* Fix TF Serving when output_hidden_states and output_attentions are True

* Add tests for saved model creation + bug fix for multiple choices models

* remove unused import

* Fix the input for several layers

* Fix test

* Fix conflict printing

* Apply style

* Fix XLM and Flaubert for TensorFlow

* Apply style

* Fix TF check version

* Apply style

* Trigger CI

* Fixed empty asserts

* black-reformatted stragglers in templates

* More code quality checks

* Update src/transformers/convert_marian_to_pytorch.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/convert_marian_to_pytorch.py
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* removed unused line as per @sshleifer
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* Add script to convert tf2.x checkpoint to pytorch

The script converts the newer TF2.x checkpoints (as published on their official GitHub: https://github.com/tensorflow/models/tree/master/official/nlp/bert) to Pytorch.

* rename file in order to stay consistent with naming convention

- remove duplicate doc-entry for tokenize_chinese_chars
- add doc for strip_accents and wordpieces_prefix

Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* Add bart-large-mnli-yahoo-answers model card

* Add examples

* Add widget example

* Rm bart tag
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Use return_dict=True in all tests

* Formatting

* Harmonize both Trainers API

* Fix test

* main_prcess -> process_zero

* readme m3hrdadfi/albert-fa-base-v2

model_card readme for m3hrdadfi/albert-fa-base-v2

* Update model_cards/m3hrdadfi/albert-fa-base-v2/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* add parse_dict to parse arguments from dict

* add unit test for parse_dict

* Replace mecab-python3 with fugashi

This replaces mecab-python3 with fugashi for Japanese tokenization. I am
the maintainer of both projects.

Both projects are MeCab wrappers, so the underlying C++ code is the
same. fugashi is the newer wrapper and doesn't use SWIG, so for basic
use of the MeCab API it's easier to use.

This code insures the use of a version of ipadic installed via pip,
which should make versioning and tracking down issues easier.

fugashi has wheels for Windows, OSX, and Linux, which will help with
issues with installing old versions of mecab-python3 on Windows.
Compared to mecab-python3, because fugashi doesn't use SWIG, it doesn't
require a C++ runtime to be installed on Windows.

In adding this change I removed some code dealing with `cursor`,
`token_start`, and `token_end` variables. These variables didn't seem to
be used for anything, it is unclear to me why they were there.

I ran the tests...

* enable easy checkout switch

allow having multiple repository checkouts and not needing to remember to rerun 'pip install -e .[dev]' when switching between checkouts and running tests.

* make isort happy

* examples needs one too

* fixed type; add Pytorch Native CUDA AMP support

* reverted commit on modeling_utils

* confirming to HF black formatting rule

* changed bool value of _use_apex

* scaler support for gradient clipping

* fix inplace operation of clip_grad_norm

* removed not while version comparison