GitLab

* fix auto model causal lm

* leverage given functionality

* apply unused kwargs to all auto models

[Reformer classification head] Implement the reformer model classification head for text classification (#5198)

* Reformer model head classification implementation for text classification

* Reformat the reformer model classification code

* PR review comments, and test case implementation for reformer for classification head changes

* CI/CD reformer for classification head test import error fix

* CI/CD test case implementation  added ReformerForSequenceClassification to all_model_classes

* Code formatting- fixed

* Normal test cases added for reformer classification head

* Fix test cases implementation for the reformer classification head

* removed token_type_id parameter from the reformer classification head

* fixed the test case for reformer classification head

* merge conflict with master fixed

* merge conflict, changed reformer classification to accept the choice_label parameter added in latest code

* refactored the the reformer classification head test code

* reformer classification head, common transform test cases fixed

* final set of the review comment, rearranging the reformer classes and docstring add to classification forward method

* fixed the compilation error and text case fix for reformer classification head

* Apply suggestions from code review

Remove unnecessary dup
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

Minor doc fix.

* implement FlaubertForTokenClassification as a subclass of XLMForTokenClassification

* fix mapping order

* add the doc

* add common tests

* fix longformer global attention output

* fix multi gpu problem

* replace -10000 with 0

* better comment

* make attention output equal local and global

* Update src/transformers/modeling_longformer.py

* Fix Trainer in DataParallel setting

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

* Add model type check for pipelines

* Add model type check for pipelines

* rename func

* Fix the init parameters

* Fix format

* rollback unnecessary refactor

* Document model outputs

* Update docs/source/main_classes/output.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

Fixed use of memories in XLNet (caching for language generation + warning when loading improper memoryless model) (#5632)

* Pytorch gpu => cpu proper device

* Memoryless XLNet warning + fixed memories during generation

* Revert "Pytorch gpu => cpu proper device"

This reverts commit 93489b36

* made black happy

* TF generation with memories

* dim => axis

* added padding_text to TF XL models

* Added comment, added TF

* [WIP] Proposal for model outputs

* All Bert models

* Make CI green maybe?

* Fix ONNX test

* Isolate ModelOutput from pt and tf

* Formatting

* Add Electra models

* Auto-generate docstrings from outputs

* Add TF outputs

* Add some BERT models

* Revert TF side

* Remove last traces of TF changes

* Fail with a clear error message

* Add Albert and work through Bart

* Add CTRL and DistilBert

* Formatting

* Progress on Bart

* Renames and finish Bart

* Formatting

* Fix last test

* Add DPR

* Finish Electra and add FlauBERT

* Add GPT2

* Add Longformer

* Add MMBT

* Add MobileBert

* Add GPT

* Formatting

* Add Reformer

* Add Roberta

* Add T5

* Add Transformer XL

* Fix test

* Add XLM + fix XLMForTokenClassification

* Style + XLMRoberta

* Add XLNet

* Formatting

* Add doc of return_tuple arg

* Update PretrainedConfig doc

* Formatting

* Small fixes

* Forgotten args and more cleanup

* Pytorch gpu => cpu proper device

* Memoryless XLNet warning + fixed memories during generation

* Revert "Memoryless XLNet warning + fixed memories during generation"

This reverts commit 3d3251ff

* Took the operations on the generated_sequence out of the ensure_device scope

* Test XLA examples

* Style

* Using `require_torch_tpu`

* Style

* No need for pytest

* Ensure padding and question cannot have higher probs than context.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Add bart the the list of tokenizers adding two <sep> tokens for squad_convert_example_to_feature
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Format.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Addressing @patrickvonplaten comments.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Addressing @patrickvonplaten comments about masking non-context element when generating the answer.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Addressing @sshleifer comments.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Make sure we mask CLS after handling impossible answers
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Mask in the correct vectors ...
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Add B I handling to grouping

* Add fix to include separate entity as last token

* move last_idx definition outside loop

* Use first entity in entity group as reference for entity type

* Add test cases

* Take out extra class accidentally added

* Return tf ner grouped test to original

* Take out redundant last entity

* Get last_idx safely
Co-authored-by: ColleterVi <36503688+ColleterVi@users.noreply.github.com>

* Fix first entity comment

* Create separate functions for group_sub_entities and group_entities (splitting call method to testable functions)

* Take out unnecessary last_idx

* Remove additional forward pass test

* Move token classification basic tests to separate class

* Move token classification basic tests back to monocolumninputtestcase

* Move base ner tests to nerpipelinetests

* Take out unused kwargs

* Add back mandatory_keys argument

* Add unitary tests for group_entities in _test_ner_pipeline

* Fix last entity handling

* Fix grouping fucntion used

* Add typing to group_sub_entities and group_entities
Co-authored-by: ColleterVi <36503688+ColleterVi@users.noreply.github.com>

* tf_train

* adapt timing for tpu

* fix timing

* fix timing

* fix timing

* fix timing

* update notebook

* add tests

improve unittests for finetuning, especially w.r.t testing frozen parameters
fix freeze_embeds for T5
add streamlit setup.cfg

[Almost all TF models] TF clean up: add missing CLM / MLM loss; fix T5 naming and keras compile (#5395)

* add first version of clm tf

* make style

* add more tests for bert

* update tf clm loss

* fix tests

* correct tf ner script

* add mlm loss

* delete bogus file

* clean tf auto model + add tests

* finish adding clm loss everywhere

* fix training in distilbert

* fix flake8

* save intermediate

* fix tf t5 naming

* remove prints

* finish up

* up

* fix tf gpt2

* fix new test utils import

* fix flake8

* keep backward compatibility

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

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

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

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

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

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

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

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

* apply sylvains suggestions
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix test imports

* fix max_length

* style

* fix tests

* add SquadDataset

* add DataCollatorForQuestionAnswering

* update __init__

* add run_squad with  trainer

* add DataCollatorForQuestionAnswering in __init__

* pass data_collator to trainer

* doc tweak

* Update run_squad_trainer.py

* Update __init__.py

* Update __init__.py
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* beginning of dpr modeling

* wip

* implement forward

* remove biencoder + better init weights

* export dpr model to embed model for nlp lib

* add new api

* remove old code

* make style

* fix dumb typo

* don't load bert weights

* docs

* docs

* style

* move the `k` parameter

* fix init_weights

* add pretrained configs

* minor

* update config names

* style

* better config

* style

* clean code based on PR comments

* change Dpr to DPR

* fix config

* switch encoder config to a dict

* style

* inheritance -> composition

* add messages in assert startements

* add dpr reader tokenizer

* one tokenizer per model

* fix base_model_prefix

* fix imports

* typo

* add convert script

* docs

* change tokenizers conf names

* style

* change tokenizers conf names

* minor

* minor

* fix wrong names

* minor

* remove unused convert functions

* rename convert script

* use return_tensors in tokenizers

* remove n_questions dim

* move generate logic to tokenizer

* style

* add docs

* docs

* quality

* docs

* add tests

* style

* add tokenization tests

* DPR full tests

* Stay true to the attention mask building

* update docs

* missing param in bert input docs

* docs

* style
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

* Default decoder inputs to encoder ones for T5 if neither are specified.

* Fixing typo, now all tests are passing.

* Changing einsum to operations supported by onnx

* Adding a test to ensure T5 can be exported to onnx op>9

* Modified test for onnx export to make it faster

* Styling changes.

* Styling changes.

* Changing notation for matrix multiplication
Co-authored-by: Abel Riboulot <tkai@protomail.com>

* fix attention mask

* fix slow test

* refactor attn masks

* fix fp16 generate test

* Added data collator for XLNet language modeling and related calls

Added DataCollatorForXLNetLanguageModeling in data/data_collator.py
to generate necessary inputs for language modeling training with
XLNetLMHeadModel. Also added related arguments, logic and calls in
examples/language-modeling/run_language_modeling.py.

Resolves: #4739, #2008 (partially)

* Changed name to `DataCollatorForPermutationLanguageModeling`

Changed the name of `DataCollatorForXLNetLanguageModeling` to the more general `DataCollatorForPermutationLanguageModelling`.
Removed the `--mlm` flag requirement for the new collator and defined a separate `--plm_probability` flag for its use.
CTRL uses a CLM loss just like GPT and GPT-2, so should work out of the box with this script (provided `past` is taken care of
similar to `mems` for XLNet).
Changed calls and imports appropriately.

* Added detailed comments, changed variable names

Added more detailed comments to `DataCollatorForPermutationLanguageModeling` in `data/data_collator.py` to explain working. Also cleaned up variable names and made them more informative.

* Added tests for new data collator

Added tests in `tests/test_trainer.py` for DataCollatorForPermutationLanguageModeling based on those in DataCollatorForLanguageModeling. A specific test has been added to check for odd-length sequences.

* Fixed styling issues

* BertTokenizerFast - Do not specify strip_accents by default

* Bump tokenizers to new version

* Add test for AddedToken serialization

* Fix #5507

* Fix formatting

* GPT2 tokenizer should not output token type IDs

* Same for OpenAIGPT