* [WIP] SP tokenizers
* fixing tests for T5
* WIP tokenizers
* serialization
* update T5
* WIP T5 tokenization
* slow to fast conversion script
* Refactoring to move tokenzier implementations inside transformers
* Adding gpt - refactoring - quality
* WIP adding several tokenizers to the fast world
* WIP Roberta - moving implementations
* update to dev4 switch file loading to in-memory loading
* Updating and fixing
* advancing on the tokenizers - updating do_lower_case
* style and quality
* moving forward with tokenizers conversion and tests
* MBart, T5
* dumping the fast version of transformer XL
* Adding to autotokenizers + style/quality
* update init and space_between_special_tokens
* style and quality
* bump up tokenizers version
* add protobuf
* fix pickle Bert JP with Mecab
* fix newly added tokenizers
* style and quality
* fix bert japanese
* fix funnel
* limite tokenizer warning to one occurence
* clean up file
* fix new tokenizers
* fast tokenizers deep tests
* WIP adding all the special fast tests on the new fast tokenizers
* quick fix
* adding more fast tokenizers in the fast tests
* all tokenizers in fast version tested
* Adding BertGenerationFast
* bump up setup.py for CI
* remove BertGenerationFast (too early)
* bump up tokenizers version
* Clean old docstrings
* Typo
* Update following Lysandre comments
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>
* Replaced torch.load for loading the pretrained vocab of TransformerXL to pickle.load
* Replaced torch.save with pickle.dump when saving the vocabulary
* updating transformer-xl
* uploaded on S3 - compatibility
* fix tests
* style
* Address review comments
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* [Model card] SinhalaBERTo model.
This is the model card for keshan/SinhalaBERTo model.
* Update model_cards/keshan/SinhalaBERTo/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
* Initial callback proposal
* Finish various callbacks
* Post-rebase conflicts
* Fix tests
* Don't use something that's not set
* Documentation
* Remove unwanted print.
* Document all models can work
* Add tests + small fixes
* Update docs/source/internal/trainer_utils.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Address review comments
* Fix TF tests
* Real fix this time
* This one should work
* Fix typo
* Really fix typo
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
- Use cuda:10.2 image instead of 10.1 (to address version mismatch
warning with pytorch)
- Use devel version that is built on the runtime and includes headers
and development tools (was otherwise failing to build apex)
* Create README.md
Model description for all LEGAL-BERT models, published as part of "LEGAL-BERT: The Muppets straight out of Law School". Chalkidis et al., 2018, In Findings of EMNLP 2020
* Update model_cards/nlpaueb/legal-bert-base-uncased/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
* Fixing top_k and min_length assertions, and a typo fix
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* PoC on RAG
* Format class name/obj name
* Better name in message
* PoC on one TF model
* Add PyTorch and TF dummy objects + script
* Treat scikit-learn
* Bad copy pastes
* Typo
'The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.'
I dont know how to change the 'How to use this model directly from the 🤗/transformers library:' part since it is not part of the model-paper
* 🚩 Add `power` argument for TF PolynomialDecay
* 🚩 Create default optimizer with power
* 🚩 Add argument to training args
* 🚨 Clean code format
* 🚨 Fix black warning
* 🚨 Fix code format
* configuration_squeezebert.py
thin wrapper around bert tokenizer
fix typos
wip sb model code
wip modeling_squeezebert.py. Next step is to get the multi-layer-output interface working
set up squeezebert to use BertModelOutput when returning results.
squeezebert documentation
formatting
allow head mask that is an array of [None, ..., None]
docs
docs cont'd
path to vocab
docs and pointers to cloud files (WIP)
line length and indentation
squeezebert model cards
formatting of model cards
untrack modeling_squeezebert_scratchpad.py
update aws paths to vocab and config files
get rid of stub of NSP code, and advise users to pretrain with mlm only
fix rebase issues
redo rebase of modeling_auto.py
fix issues with code formatting
more code format auto-fixes
move squeezebert before bert in tokenization_auto.py and modeling_auto.py because squeezebert inherits from bert
tests for squeezebert modeling and tokenization
fix typo
move squeezebert before bert in modeling_auto.py to fix inheritance problem
disable test_head_masking, since squeezebert doesn't yet implement head masking
fix issues exposed by the test_modeling_squeezebert.py
fix an issue exposed by test_tokenization_squeezebert.py
fix issue exposed by test_modeling_squeezebert.py
auto generated code style improvement
issue that we inherited from modeling_xxx.py: SqueezeBertForMaskedLM.forward() calls self.cls(), but there is no self.cls, and I think the goal was actually to call self.lm_head()
update copyright
resolve failing 'test_hidden_states_output' and remove unused encoder_hidden_states and encoder_attention_mask
docs
add integration test. rename squeezebert-mnli --> squeezebert/squeezebert-mnli
autogenerated formatting tweaks
integrate feedback from patrickvonplaten and sgugger to programming style and documentation strings
* tiny change to order of imports
