GitLab

We currently create encoder attention masks (when they're not provided)
based on the shape of the inputs to the encoder. This is obviously
wrong; sequences can be of different lengths. We now create the encoder
attention mask based on the batch_size and sequence_length of the
encoder hidden states.

* Switch to plain unittest for skipping slow tests.

Add a RUN_SLOW environment variable for running them.

* Switch to plain unittest for PyTorch dependency.

* Switch to plain unittest for TensorFlow dependency.

* Avoid leaking open files in the test suite.

This prevents spurious warnings when running tests.

* Fix unicode warning on Python 2 when running tests.

The warning was:

    UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal

* Support running PyTorch tests on a GPU.

Reverts 27e015bd.

* Tests no longer require pytest.

* Make tests pass on cuda

* [cli] Uploads: add progress bar

see https://github.com/huggingface/transformers/pull/2044#discussion_r354057827 for context

* rename + documentation

* Add auto-referential comment

corrected documentation for past tensor shape for ctrl and gpt2 model

Add NER TF2 example.

Nicer error message when Bert's input is missing batch size

* license

* changes

* ok

* Update paper link and commands to run

* pointer to uber repo

Remove dead code in tests.

Fixing camembert tokenization

typo fix on the docs as per Pytorch v1.1+

fixed XLNet attention output for both attention streams whenever target_mapping is provided

Do not use GPU when importing transformers

XLNet for Token classification

CLI for authenticated file sharing

fix summary_type value of SequenceSummary

Create a NER example similar to the Pytorch one. It takes the same options, and can be run the same way.