pipeline.
- If table is empty then the line that contain `answer[0]` will fail.
- This PR add a check to prevent `answer[0]`.
- Also adds an early check for presence of `table` and `query` to
prevent late failure and give better error message.
- Adds a few tests to make sure these errors are correctly raised.
* We most likely don't want special tokens in this output.
* Adding `skip_special_tokens=True` to FillMaskPipeline
- It's backward incompatible.
- It makes for sense for pipelines to remove references to
special_tokens (all of the other pipelines do that).
- Keeping special tokens makes it hard for users to actually remove them
because all models have different tokens (<s>, <cls>, [CLS], ....)
* Fixing `token_str` in the same vein, and actually fix the tests too !
* Add head_mask/decoder_head_mask for TF BART models
* Add head_mask and decoder_head_mask input arguments for TF BART-based
models as a TF counterpart to the PR #9569
* Add test_headmasking functionality to tests/test_modeling_tf_common.py
* TODO: Add a test to verify that we can get a gradient back for
importance score computation
* Remove redundant #TODO note
Remove redundant #TODO note from tests/test_modeling_tf_common.py
* Fix assertions
* Make style
* Fix ...Model input args and adjust one new test
* Add back head_mask and decoder_head_mask to BART-based ...Model
after the last commit
* Remove head_mask ande decoder_head_mask from input_dict
in TF test_train_pipeline_custom_model as these two have different
shape than other input args (Necessary for passing this test)
* Revert adding global_rng in test_modeling_tf_common.py
* Add decoder_head_mask for PyTorch T5 model
* Add decoder_head_mask args into T5Model and T5ForConditionalGeneration
* Slightly change the order of input args to be in accordance
with the convention from BART-based models introduced within the PR #9569.
* Make style for modeling_t5.py
* Add decoder_head_mask for TF T5 models
* Separate head_mask and decoder_head_mask args in TF T5 models
* Slightly change the order of input args to follow convention
of BART-based models updated in PR #9569
* Update test_forward_signature tests/test_modeling_tf_common.py
w.r.t. the changed order of input args
* Add FutureWarnings for T5 and TFT5 models
* Add FutureWarnings for T5 and TFT5 models warning a user that
input argument `head_mask` was split into two arguments -
`head_mask` and `decoder_head_mask`
* Add default behaviour - `decoder_head_mask` is set to copy
`head_mask`
* Fix T5 modeling and FutureWarning
* Make proper usage of head_mask and decoder_head_mask
in cross_attention
* Fix conditions for raising FutureWarning
* Reformat FutureWarning in T5 modeling
* Refactor the warning message
* Update past_key_values in gpt2 (#9391)
* Update generation_utils, and rename some items
* Update modeling_gpt2 to avoid an error in gradient_checkpointing
* Remove 'reorder_cache' from util and add variations to XLNet, TransfoXL, GPT-2
* Change the location of '_reorder_cache' in modeling files
* Add '_reorder_cache' in modeling_ctrl
* Fix a bug of my last commit in CTRL
* Add '_reorder_cache' to GPT2DoubleHeadsModel
* Manage 'use_cache' in config of test_modeling_gpt2
* Clean up the doc string
* Update src/transformers/models/gpt2/modeling_gpt2.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fix the doc string (GPT-2, CTRL)
* improve gradient_checkpointing_behavior
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add head_mask/decoder_head_mask for BART
This branch implement head_mask and decoder_head_mask
for BART-based models. Full list below:
- BART
- MBart
- Blenderbot
- BlenderbotSmall
- Marian
- Pegasus
Everything is accompanied with updated testing.
* Fix test_headmasking for BART models
* Fix text_headmasking for BART-like models
which has only 2 layers in each modules.
The condition
```
self.assertNotEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
```
is, therefore, invalid for encoder-decoder models considering
the `head_mask`
```
head_mask = torch.ones(
self.model_tester.num_hidden_layers,
self.model_tester.num_attention_heads,
device=torch_device,
)
head_mask[0, 0] = 0
head_mask[-1, :-1] = 0
```
specified in the `test_headmasking` test/function.
* Adjust test_modeling_common.py to reflect T5 input args
* Update tests/test_modeling_common.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* make style
* make fix-copies
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add target contextmanager and rework prepare_seq2seq_batch
* Fix tests, treat BART and Barthez
* Add last tokenizers
* Fix test
* Set src token before calling the superclass
* Remove special behavior for T5
* Remove needless imports
* Remove needless asserts
* Add LayoutLMForSequenceClassification and integration tests
Improve docs
Add LayoutLM notebook to list of community notebooks
* Make style & quality
* Address comments by @sgugger, @patrickvonplaten and @LysandreJik
* Fix rebase with master
* Reformat in one line
* Improve code examples as requested by @patrickvonplaten
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Enable TruncationStrategy override for pipelines
* Update isort.
* Fixing test
* Fixing text_generation pipeline.
* Using same DummyTok as other PR for easier merge later.
* Some more import guards.
* Remove bogus file.
* Do not pass `generate_kwargs` to `_parse_and_tokenize`.
@patrickvonplaten
* Removed DummyTok.
* Doc quality.
* Cleaning up conversation tests.
* Adding tests that don't require downloading models + conversation can be
fully created from static state.
* Making tests non flaky (by fixing generation length)
* Bumping isort version.
* Doc cleanup.
* Remove unused test in this PR.
* Torch import guard for TF.
* Missing torch guard.
* Small mistake in doc.
* Actual uses `_history` and `_index` cache.
+ remove dead enumerate
+ improve warning message.
* Update src/transformers/pipelines/conversational.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/pipelines/conversational.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/pipelines/conversational.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Adding comments and cleaner code to address history copy.
* Improving pipeline name in tests.
* Change tokenizer to a real one (still created at runtime with no
external dependency)
* Simplify DummyTok, reverse changes on tokenization.
* Removing DummyTok.
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Don't import libs to check they are available
* Don't import integrations at init
* Add importlib_metdata to deps
* Remove old vars references
* Avoid syntax error
* Adapt testing utils
* Try to appease torchhub
* Add dependency
* Remove more private variables
* Fix typo
* Another typo
* Refine the tf availability test
* Define new output dataclasses for greedy generation
* Add output_[...] flags in greedy generation methods
Added output_attentions, output_hidden_states, output_scores flags in
generate and greedy_search methods in GenerationMixin.
* [WIP] Implement logic and tests for output flags in generation
* Update GreedySearchOutput classes & docstring
* Implement greedy search output accumulation logic
Update greedy_search unittests
Fix generate method return value docstring
Properly init flags with the default config
* Update configuration to add output_scores flag
* Fix test_generation_utils
Sort imports and fix isinstance tests for GreedySearchOutputs
* Fix typo in generation_utils
* Add return_dict_in_generate for backwards compatibility
* Add return_dict_in_generate flag in config
* Fix tyPo in configuration
* Fix handling of attentions and hidden_states flags
* Make style & quality
* first attempt attentions
* some corrections
* improve tests
* special models requires special test
* disable xlm test for now
* clean tests
* fix for tf
* isort
* Add output dataclasses for other generation methods
* Add logic to return dict in sample generation
* Complete test for sample generation
- Pass output_attentions and output_hidden_states flags to encoder in
encoder-decoder models
- Fix import satements order in test_generation_utils file
* Add logic to return dict in sample generation
- Refactor tests to avoid using self.assertTrue, which provides
scarce information when the test fails
- Add tests for the three beam_search methods: vanilla, sample and
grouped
* Style doc
* Fix copy-paste error in generation tests
* Rename logits to scores and refactor
* Refactor group_beam_search for consistency
* make style
* add sequences_scores
* fix all tests
* add docs
* fix beam search finalize test
* correct docstring
* clean some files
* Made suggested changes to the documentation
* Style doc ?
* Style doc using the Python util
* Update src/transformers/generation_utils.py
* fix empty lines
* fix all test
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* create model
* add integration
* save current state
* make integration tests pass
* add one more test
* add explanation to tests
* remove from bart
* add padding
* remove unnecessary test
* make all tests pass
* re-add cookie cutter tests
* finish PyTorch
* fix attention test
* Update tests/test_modeling_common.py
* revert change
* remove unused file
* add string to doc
* save intermediate
* make tf integration tests pass
* finish tf
* fix doc
* fix docs again
* add led to doctree
* add to auto tokenizer
* added tips for led
* make style
* apply jplus statements
* correct tf longformer
* apply lysandres suggestions
* apply sylvains suggestions
* Apply suggestions from code review
* Create modeling_tf_dpr.py
* Add TFDPR
* Add back TFPegasus, TFMarian, TFMBart, TFBlenderBot
last commit accidentally deleted these 4 lines, so I recover them back
* Add TFDPR
* Add TFDPR
* clean up some comments, add TF input-style doc string
* Add TFDPR
* Make return_dict=False as default
* Fix return_dict bug (in .from_pretrained)
* Add get_input_embeddings()
* Create test_modeling_tf_dpr.py
The current version is already passed all 27 tests!
Please see the test run at :
https://colab.research.google.com/drive/1czS_m9zy5k-iSJbzA_DP1k1xAAC_sdkf?usp=sharing
* fix quality
* delete init weights
* run fix copies
* fix repo consis
* del config_class, load_tf_weights
They shoud be 'pytorch only'
* add config_class back
after removing it, test failed ... so totally only removing "use_tf_weights = None" on Lysandre suggestion
* newline after .. note::
* import tf, np (Necessary for ModelIntegrationTest)
* slow_test from_pretrained with from_pt=True
At the moment we don't have TF weights (since we don't have official official TF model)
Previously, I did not run slow test, so I missed this bug
* Add simple TFDPRModelIntegrationTest
Note that this is just a test that TF and Pytorch gives approx. the same output.
However, I could not test with the official DPR repo's output yet
* upload correct tf model
* remove position_ids as missing keys
* fix RagSeq generate with context_input_ids
fix RagSeq generate with context_input_ids
* apply style
* delete unused lines
* Add test_rag_sequence_generate_batch_from_context_input_ids
* Readability improved
* stylying
* Stylize
* typos
* add check_model_generate_from_context_input_ids
* make style
* Apply suggestions from code review
* make style2
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: patrickvonplaten <patrick@huggingface.co>
* add past_key_values
* add use_cache option
* make mask before cutting ids
* adjust position_ids according to past_key_values
* flatten past_key_values
* fix positional embeds
* fix _reorder_cache
* set use_cache to false when not decoder, fix attention mask init
* add test for caching
* add past_key_values for Roberta
* fix position embeds
* add caching test for roberta
* add doc
* make style
* doc, fix attention mask, test
* small fixes
* adress patrick's comments
* input_ids shouldn't start with pad token
* use_cache only when decoder
* make consistent with bert
* make copies consistent
* add use_cache to encoder
* add past_key_values to tapas attention
* apply suggestions from code review
* make coppies consistent
* add attn mask in tests
* remove copied from longformer
* apply suggestions from code review
* fix bart test
* nit
* simplify model outputs
* fix doc
* fix output ordering
* Add label smoothing in Trainer
* Add options for scheduler and Adafactor in Trainer
* Put Seq2SeqTrainer in the main lib
* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Address review comments and adapt scripts
* Documentation
* Move test not using script to tests folder
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add new run_swag example
* Add check
* Add sample
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Very important change to make Lysandre happy
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* First commit: adding all files from tapas_v3
* Fix multiple bugs including soft dependency and new structure of the library
* Improve testing by adding torch_device to inputs and adding dependency on scatter
* Use Python 3 inheritance rather than Python 2
* First draft model cards of base sized models
* Remove model cards as they are already on the hub
* Fix multiple bugs with integration tests
* All model integration tests pass
* Remove print statement
* Add test for convert_logits_to_predictions method of TapasTokenizer
* Incorporate suggestions by Google authors
* Fix remaining tests
* Change position embeddings sizes to 512 instead of 1024
* Comment out positional embedding sizes
* Update PRETRAINED_VOCAB_FILES_MAP and PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
* Added more model names
* Fix truncation when no max length is specified
* Disable torchscript test
* Make style & make quality
* Quality
* Address CI needs
* Test the Masked LM model
* Fix the masked LM model
* Truncate when overflowing
* More much needed docs improvements
* Fix some URLs
* Some more docs improvements
* Test PyTorch scatter
* Set to slow + minify
* Calm flake8 down
* First commit: adding all files from tapas_v3
* Fix multiple bugs including soft dependency and new structure of the library
* Improve testing by adding torch_device to inputs and adding dependency on scatter
* Use Python 3 inheritance rather than Python 2
* First draft model cards of base sized models
* Remove model cards as they are already on the hub
* Fix multiple bugs with integration tests
* All model integration tests pass
* Remove print statement
* Add test for convert_logits_to_predictions method of TapasTokenizer
* Incorporate suggestions by Google authors
* Fix remaining tests
* Change position embeddings sizes to 512 instead of 1024
* Comment out positional embedding sizes
* Update PRETRAINED_VOCAB_FILES_MAP and PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
* Added more model names
* Fix truncation when no max length is specified
* Disable torchscript test
* Make style & make quality
* Quality
* Address CI needs
* Test the Masked LM model
* Fix the masked LM model
* Truncate when overflowing
* More much needed docs improvements
* Fix some URLs
* Some more docs improvements
* Add add_pooling_layer argument to TapasModel
Fix comments by @sgugger and @patrickvonplaten
* Fix issue in docs + fix style and quality
* Clean up conversion script and add task parameter to TapasConfig
* Revert the task parameter of TapasConfig
Some minor fixes
* Improve conversion script and add test for absolute position embeddings
* Improve conversion script and add test for absolute position embeddings
* Fix bug with reset_position_index_per_cell arg of the conversion cli
* Add notebooks to the examples directory and fix style and quality
* Apply suggestions from code review
* Move from `nielsr/` to `google/` namespace
* Apply Sylvain's comments
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Rogge Niels <niels.rogge@howest.be>
Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* add model parallelism to T5EncoderModel
add model parallelism to T5EncoderModel
* remove decoder from T5EncoderModel parallelize
* uodate T5EncoderModel docs
* Extend T5ModelTest for T5EncoderModel
* fix T5Stask using range for get_device_map
* fix style
Co-authored-by: Ahmed Elnaggar <elnaggar@rostlab.informatik.tu-muenchen.de>
* Resize the biases in same time than the embeddings
* Trigger CI
* Biases are not reset anymore
* Remove get_output_embeddings + better LM model detection in generation utils
* Apply style
* First test on BERT
* Update docstring + new name
* Apply the new resizing logic to all the models
* fix tests
* Apply style
* Update the template
* Fix naming
* Fix naming
* Apply style
* Apply style
* Remove unused import
* Revert get_output_embeddings
* Trigger CI
* Update num parameters
* Restore get_output_embeddings in TFPretrainedModel and add comments
* Style
* Add decoder resizing
* Style
* Fix tests
* Separate bias and decoder resize
* Fix tests
* Fix tests
* Apply style
* Add bias resizing in MPNet
* Trigger CI
* Apply style
* remove make on the fly linear embedding
* start refactor
* big first refactor
* save intermediate
* save intermediat
* correct mask issue
* save tests
* refactor padding masks
* make all tests pass
* further refactor
* make pegasus test pass
* fix bool if
* fix leftover tests
* continue
* bart renaming
* delete torchscript test hack
* fix imports in tests
* correct shift
* fix docs and repo cons
* re-add fix for FSTM
* typo in test
* fix typo
* fix another typo
* continue
* hot fix 2 for tf
* small fixes
* refactor types linting
* continue
* finish refactor
* fix import in tests
* better bart names
* further refactor and add test
* delete hack
* apply sylvains and lysandres commens
* small perf improv
* further perf improv
* improv perf
* fix typo
* make style
* small perf improv
* Remove "Model" suffix from Flax models to look more 🤗
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Initial working (forward + backward) for Flax MLM training example.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Simply code
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing comments, using module and moving to LM task.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Restore parameter name "module" wrongly renamed model.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Restore correct output ordering...
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Actually commit the example 😅
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Add FlaxBertModelForMaskedLM after rebasing.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Make it possible to initialize the training from scratch
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Reuse flax linen example of cross entropy loss
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Added specific data collator for flax
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Remove todo for data collator
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Added evaluation step
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Added ability to provide dtype to support bfloat16 on TPU
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Enable flax tensorboard output
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Enable jax.pmap support.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Ensure batches are correctly sized to be dispatched with jax.pmap
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Enable bfloat16 with --fp16 cmdline args
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Correctly export metrics to tensorboard
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Added dropout and ability to use it.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Effectively enable & disable during training and evaluation steps.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Oops.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Enable specifying kernel initializer scale
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Style.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Added warmup step to the learning rate scheduler.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Fix typo.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Print training loss
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Make style
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* fix linter issue (flake8)
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Fix model matching
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Fix dummies
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Fix non default dtype on Flax models
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Use the same create_position_ids_from_input_ids for FlaxRoberta
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Make Roberta attention as Bert
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* fix copy
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Wording.
Co-authored-by: Marc van Zee <marcvanzee@gmail.com>
Co-authored-by: Marc van Zee <marcvanzee@gmail.com>
* diverse beam search
* bug fixes
* bug fixes
* bug fix
* separate out diverse_beam_search function
* separate out diverse_beam_search function
* bug fix
* improve code quality
* bug fix
* bug fix
* separate out diverse beam search scorer
* code format
* code format
* code format
* code format
* add test
* code format
* documentation changes
* code quality
* add slow integration tests
* more general name
* refactor into logits processor
* add test
* avoid too much copy paste
* refactor
* add to docs
* fix-copies
* bug fix
* Revert "bug fix"
This reverts commit c99eb5a8dc.
* improve comment
* implement sylvains feedback
Co-authored-by: Ayush Jain <a.jain@sprinklr.com>
Co-authored-by: ayushtiku5 <40797286+ayushtiku5@users.noreply.github.com>
* Add new SQUAD example
* Same with a task-specific Trainer
* Address review comment.
* Small fixes
* Initial work for XLNet
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Final clean up and working XLNet script
* Test and debug
* Final working version
* Add new SQUAD example
* Same with a task-specific Trainer
* Address review comment.
* Small fixes
* Initial work for XLNet
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Final clean up and working XLNet script
* Test and debug
* Final working version
* Add tick
* Update README
* Address review comments
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Removed unused `encoder_hidden_states` and `encoder_attention_mask` from MobileBert
* Removed decoder tests for MobileBert
* Removed now unnecessary import
* initial commit
* [cli] lfs commands
* Fix FileSlice
* Tweak to FileSlice
* [hf_api] Backport filetype arg from `datasets`
cc @lhoestq
* Silm down the CI while i'm working
* Ok let's try this in CI
* Update config.yml
* Do not try this at home
* one more try
* Update lfs.py
* Revert "Tweak to FileSlice"
This reverts commit d7e32c4b35.
* Update test_hf_api.py
* Update test_hf_api.py
* Update test_hf_api.py
* CI still green?
* make CI green again?
* Update test_hf_api.py
* make CI red again?
* Update test_hf_api.py
* add CI style back
* Fix CI?
* oh my
* doc + switch back to real staging endpoint
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Pierric Cistac <Pierrci@users.noreply.github.com>
* Fix docblock + f-strings
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Pierric Cistac <Pierrci@users.noreply.github.com>
* Add TFGPT2ForSequenceClassification based on DialogRPT
* Add TFGPT2ForSequenceClassification based on DialogRPT
* TFGPT2ForSequenceClassification based on DialogRPT-refactored code, implemented review comments and added input processing
* Add TFGPT2ForSequenceClassification based on DialogRPT
* TFGPT2ForSequenceClassification based on DialogRPT-refactored code, implemented review comments and added input processing
* code refactor for latest other TF PR
* code refactor
* code refactor
* Update modeling_tf_gpt2.py
* Warning about too long input for fast tokenizers too
If truncation is not set in tokenizers, but the tokenization is too long
for the model (`model_max_length`), we used to trigger a warning that
The input would probably fail (which it most likely will).
This PR re-enables the warning for fast tokenizers too and uses common
code for the trigger to make sure it's consistent across.
* Checking for pair of inputs too.
* Making the function private and adding it's doc.
* Remove formatting ?? in odd place.
* Missed uppercase.
* NerPipeline (TokenClassification) now outputs offsets of words
- It happens that the offsets are missing, it forces the user to pattern
match the "word" from his input, which is not always feasible.
For instance if a sentence contains the same word twice, then there
is no way to know which is which.
- This PR proposes to fix that by outputting 2 new keys for this
pipelines outputs, "start" and "end", which correspond to the string
offsets of the word. That means that we should always have the
invariant:
```python
input[entity["start"]: entity["end"]] == entity["entity_group"]
# or entity["entity"] if not grouped
```
* Fixing doc style
* Slightly increase tolerance between pytorch and flax output
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* test_multiple_sentences doesn't require torch
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Simplify parameterization on "jit" to use boolean rather than str
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Use `require_torch` on `test_multiple_sentences` because we pull the weight from the hub.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Rename "jit" parameter to "use_jit" for (hopefully) making it self-documenting.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Remove pytest.mark.parametrize which seems to fail in some circumstances
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Fix unused imports.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Fix style.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Give default parameters values for traced model.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Review comment: Change sentences to sequences
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fix decoder not returning hidden states from the last layer
* Resolve conflict
* Change the way to gather hidden states
* Add decoder hidden states test
* Make pytest and black happy
* Remove redundant line
* remove new line
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* fix mems in xlnet
* fix use_mems
* fix use_mem_len
* fix use mems
* clean docs
* fix tf typo
* make xlnet tf for generation work
* fix tf test
* refactor use cache
* add use cache for missing models
* correct use_cache in generate
* correct use cache in tf generate
* fix tf
* correct getattr typo
* make sylvain happy
* change in docs as well
* do not apply to cookie cutter statements
* fix tf test
* make pytorch model fully backward compatible
* bart output hidden states upstream
* same w/ decoder
* add tests
* fix prophetnet
* fix gpt2 and ctrl
* fix fstm and skip test for reformer and longformer
* fix all models
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* implement support for run-time dependency version checking
* try not escaping !
* use findall that works on py36
* small tweaks
* autoformatter worship
* simplify
* shorter names
* add support for non-versioned checks
* add deps
* revert
* tokenizers not required, check version only if installed
* make a proper distutils cmd and add make target
* tqdm must be checked before tokenizers
* workaround the DistributionNotFound peculiar setup
* handle the rest of packages in setup.py
* fully sync setup.py's install_requires - to check them all
* nit
* make install_requires more readable
* typo
* Update setup.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* restyle
* add types
* simplify
* simplify2
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Support BERT relative position embeddings
* Fix typo in README.md
* Address review comment
* Fix failing tests
* [tiny] Fix style_doc.py check by adding an empty line to configuration_bert.py
* make fix copies
* fix configs of electra and albert and fix longformer
* remove copy statement from longformer
* fix albert
* fix electra
* Add bert variants forward tests for various position embeddings
* [tiny] Fix style for test_modeling_bert.py
* improve docstring
* [tiny] improve docstring and remove unnecessary dependency
* [tiny] Remove unused import
* re-add to ALBERT
* make embeddings work for ALBERT
* add test for albert
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add early stopping patience and minimum threshold metric must improve to prevent early stopping to pytorch trainer
* Add early stopping test
* Set patience counter to 0 if best metric not defined yet
* Make early stopping a callback. Add callback event for updating the best metric for early stopping callback to trigger on.
* Run make style
* make funciton name sensible
* Improve new argument docstring wording and hope that flakey CI test passes.
* Use on_evaluation callback instead of custom. Remove some debug printing
* Move early stopping arguments and state into early stopping callback
* Run make style
* Remove old code
* Fix docs formatting. make style went rogue on me.
* Remove copied attributes and fix variable
* Add assertions on training arguments instead of mutating them. Move comment out of public docs.
* Make separate test for early stopping callback. Add test of invalid arguments.
* Run make style... I remembered before CI this time!
* appease flake8
* Add EarlyStoppingCallback to callback docs
* Make docstring EarlyStoppingCallabck match other callbacks.
* Fix typo in docs
* Make ci fail
* Try to make tests actually run?
* CI finally failing?
* Fix CI
* Revert "Fix CI"
This reverts commit ca7923be73.
* Ooops wrong one
* one more try
* Ok ok let's move this elsewhere
* Alternative to globals() (#8667)
* Alternative to globals()
* Error is raised later so return None
* Sentencepiece not installed make some tokenizers None
* Apply Lysandre wisdom
* Slightly clearer comment?
cc @sgugger
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* `disable_ngram_loss` fix for prophetnet
* add changes documentation
* fix _compute_loss to use mean reduction and -100 to masked tokens & remove unnecessary arguments
* mean label smoothing loss
* small refactor
* fix test
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
* working on LongformerForSequenceClassification
* add TFLongformerForMultipleChoice
* add TFLongformerForTokenClassification
* use add_start_docstrings_to_model_forward
* test TFLongformerForSequenceClassification
* test TFLongformerForMultipleChoice
* test TFLongformerForTokenClassification
* remove test from repo
* add test and doc for TFLongformerForSequenceClassification, TFLongformerForTokenClassification, TFLongformerForMultipleChoice
* add requested classes to modeling_tf_auto.py
update dummy_tf_objects
fix tests
fix bugs in requested classes
* pass all tests except test_inputs_embeds
* sync with master
* pass all tests except test_inputs_embeds
* pass all tests
* pass all tests
* work on test_inputs_embeds
* fix style and quality
* make multi choice work
* fix TFLongformerForTokenClassification signature
* fix TFLongformerForMultipleChoice, TFLongformerForSequenceClassification signature
* fix mult choice
* fix mc hint
* fix input embeds
* fix input embeds
* refactor input embeds
* fix copy issue
* apply sylvains changes and clean more
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Adding PrefixConstrainedLogitsProcessor
* fixing RAG and style_doc
* fixing black (v20 instead of v19)
* Improving doc in generation_logits_process.py
* Improving docs and typing in generation_utils.py
* docs improvement
* adding test and fixing doc typo
* fixing doc_len
* isort on test
* fixed test
* improve docstring a bit
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Tokenizers should be framework agnostic
* Run the slow tests
* Not testing
* Fix documentation
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Put models in subfolders
* Styling
* Fix imports in tests
* More fixes in test imports
* Sneaky hidden imports
* Fix imports in doc files
* More sneaky imports
* Finish fixing tests
* Fix examples
* Fix path for copies
* More fixes for examples
* Fix dummy files
* More fixes for example
* More model import fixes
* Is this why you're unhappy GitHub?
* Fix imports in conver command
* Use the CI to identify failing tests
* Remove from all examples and tests
* More default switch
* Fixes
* More test fixes
* More fixes
* Last fixes hopefully
* Use the CI to identify failing tests
* Remove from all examples and tests
* More default switch
* Fixes
* More test fixes
* More fixes
* Last fixes hopefully
* Run on the real suite
* Fix slow tests
* Fix passing token_type_ids during GPT2DoubleHeadsModel.generate() if used
and for GPT2LMHeadModel too
* Update tests to check token_type_ids usage in GPT2 models
* Simply insert T5Tokenizer's prepare_seq2seq_batch
* Update/Add some 'import'
* fix RunTimeError caused by '.view'
* Moves .view related error avoidance from seq2seq_trainer to inside prophetnet
* Update test_tokenization_prophetnet.py
* Format the test code with black
* Re-format the test code
* Update test_tokenization_prophetnet.py
* Add importing require_torch in the test code
* Add importing BatchEncoding in the test code
* Re-format the test code on Colab
* Fixing roberta for slow-fast tests
* WIP getting equivalence on pipelines
* slow-to-fast equivalence - working on question-answering pipeline
* optional FAISS tests
* Pipeline Q&A
* Move pipeline tests to their own test job again
* update tokenizer to add sequence id methods
* update to tokenizers 0.9.4
* set sentencepiecce as optional
* clean up squad
* clean up pipelines to use sequence_ids
* style/quality
* wording
* Switch to use_fast = True by default
* update tests for use_fast at True by default
* fix rag tokenizer test
* removing protobuf from required dependencies
* fix NER test for use_fast = True by default
* fixing example tests (Q&A examples use slow tokenizers for now)
* protobuf in main deps extras["sentencepiece"] and example deps
* fix protobug install test
* try to fix seq2seq by switching to slow tokenizers for now
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Update some tests
* Small update
* Apply style
* Use max_position_embeddings
* Create a fake attribute
* Create a fake attribute
* Update wrong name
* Wrong TransfoXL model file
* Keep the common tests agnostic
* Create modeling_tf_dpr.py
* Add TFDPR
* Add back TFPegasus, TFMarian, TFMBart, TFBlenderBot
last commit accidentally deleted these 4 lines, so I recover them back
* Add TFDPR
* Add TFDPR
* clean up some comments, add TF input-style doc string
* Add TFDPR
* Make return_dict=False as default
* Fix return_dict bug (in .from_pretrained)
* Add get_input_embeddings()
* Create test_modeling_tf_dpr.py
The current version is already passed all 27 tests!
Please see the test run at :
https://colab.research.google.com/drive/1czS_m9zy5k-iSJbzA_DP1k1xAAC_sdkf?usp=sharing
* fix quality
* delete init weights
* run fix copies
* fix repo consis
* del config_class, load_tf_weights
They shoud be 'pytorch only'
* add config_class back
after removing it, test failed ... so totally only removing "use_tf_weights = None" on Lysandre suggestion
* newline after .. note::
* import tf, np (Necessary for ModelIntegrationTest)
* slow_test from_pretrained with from_pt=True
At the moment we don't have TF weights (since we don't have official official TF model)
Previously, I did not run slow test, so I missed this bug
* Add simple TFDPRModelIntegrationTest
Note that this is just a test that TF and Pytorch gives approx. the same output.
However, I could not test with the official DPR repo's output yet
* upload correct tf model
* remove position_ids as missing keys
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: patrickvonplaten <patrick@huggingface.co>
* Add next sentence prediction loss computation
* Apply style
* Fix tests
* Add forgotten import
* Add forgotten import
* Use a new parameter
* Remove kwargs and use positional arguments
* fix typo
* rm use_cdn & references, and implement new hf_bucket_url
* I'm pretty sure we don't need to `read` this file
* same here
* [BIG] file_utils.networking: do not gobble up errors anymore
* Fix CI 😇
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Tiny doc tweak
* Add doc + pass kwarg everywhere
* Add more tests and explain
cc @sshleifer let me know if better
Co-Authored-By: Sam Shleifer <sshleifer@gmail.com>
* Also implement revision in pipelines
In the case where we're passing a task name or a string model identifier
* Fix CI 😇
* Fix CI
* [hf_api] new methods + command line implem
* make style
* Final endpoints post-migration
* Fix post-migration
* Py3.6 compat
cc @stefan-it
Thank you @stas00
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
* add training tests
* correct longformer
* fix docs
* fix some tests
* fix some more train tests
* remove ipdb
* fix multiple edge case model training
* fix funnel and prophetnet
* clean gpt models
* undo renaming of albert
* Add new token classification example
* Remove txt file
* Add test
* With actual testing done
* Less warmup is better
* Update examples/token-classification/run_ner_new.py
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
* Address review comments
* Fix test
* Make Lysandre happy
* Last touches and rename
* Rename in tests
* Address review comments
* More run_ner -> run_ner_old
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
* Output cross-attention with decoder attention output
* Update src/transformers/modeling_bert.py
* add cross-attention for t5 and bart as well
* fix tests
* correct typo in docs
* add sylvains and sams comments
* correct typo
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Make Trainer evaluation handle dynamic seq_length
* Document behavior.
* Fix test
* Better fix
* Fixes for realsies this time
* Address review comments
* Without forgetting to save...
* Output global_attentions in Longformer models
* make style
* small refactoring
* fix tests
* make fix-copies
* add for tf as well
* remove comments in test
* make fix-copies
* make style
* add docs
* make docstring pretty
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
- The issue is that with previous code we would have the following:
```python
qa_pipeline = (...)
qa_pipeline(question="Where was he born ?", context="")
-> IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)
```
The goal here is to improve this to actually return a ValueError
wherever possible.
While at it, I tried to simplify QuestionArgumentHandler's code to
make it smaller and more compat while keeping backward compat.
* Bug fix: NER pipeline shouldn't group separate entities of same type
* style fix
* [Bug Fix] Shouldn't group entities that are both 'B' even if they are same type
(B-type1 B-type1) != (B-type1 I-type1)
[Bug Fix] add an option `ignore_subwords` to ignore subsequent ##wordpieces in predictions. Because some models train on only the first token of a word and not on the subsequent wordpieces (BERT NER default). So it makes sense doing the same thing at inference time.
The simplest fix is to just group the subwords with the first wordpiece.
[TODO] how to handle ignored scores? just set them to 0 and calculate zero invariant mean ?
[TODO] handle different wordpiece_prefix ## ? possible approaches:
get it from tokenizer? but currently most tokenizers dont have a wordpiece_prefix property?
have an _is_subword(token)
[Feature add] added option to `skip_special_tokens`. Cause It was harder to remove them after grouping.
[Additional Changes] remove B/I prefix on returned grouped_entities
[Feature Request/TODO] Return indexes?
[Bug TODO] can't use fast tokenizer with grouped_entities ('BertTokenizerFast' object has no attribute 'convert_tokens_to_string')
* use offset_mapping to fix [UNK] token problem
* ignore score for subwords
* modify ner_pipeline test
* modify ner_pipeline test
* modify ner_pipeline test
* ner_pipeline change ignore_subwords default to true
* add ner_pipeline ignore_subword=False test case
* fix offset_mapping index
* fix style again duh
* change is_subword and convert_tokens_to_string logic
* merge tests with new test structure
* change test names
* remove old tests
* ner tests for fast tokenizer
* fast tokenizers have convert_tokens_to_string
* Fix the incorrect merge
Co-authored-by: Ceyda Cinarel <snu-ceyda@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* make it possible to invoke testconf.py in both test suites without crashing on having the same option added
* perl -pi -e 's|--make_reports|--make-reports|' to be consistent with other opts
* add `pytest --make-reports` to all CIs (and artifacts)
* fix
* Updated ConversationalPipeline to work with encoder-decoder models (e.g. BlenderBot)
* Addition of integration test for EncoderDecoder conversation model
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* [FIX] TextGenerationPipeline is currently broken.
It's most likely due to #8180.
What's missing is a multi vs single string handler at the beginning of
the pipe.
And also there was no testing of this pipeline.
* Fixing Conversational tests too.
* first draft
* show design proposition for new generate method
* up
* make better readable
* make first version
* gpt2 tests pass
* make beam search for gpt2 work
* add first encoder-decoder code
* delete typo
* make t5 work
* save indermediate
* make bart work with beam search
* finish beam search bart / t5
* add default kwargs
* make more tests pass
* fix no bad words sampler
* some fixes and tests for all distribution processors
* fix test
* fix rag slow tests
* merge to master
* add nograd to generate
* make all slow tests pass
* speed up generate
* fix edge case bug
* small fix
* correct typo
* add type hints and docstrings
* fix typos in tests
* add beam search tests
* add tests for beam scorer
* fix test rag
* finish beam search tests
* move generation tests in seperate file
* fix generation tests
* more tests
* add aggressive generation tests
* fix tests
* add gpt2 sample test
* add more docstring
* add more docs
* finish doc strings
* apply some more of sylvains and sams comments
* fix some typos
* make fix copies
* apply lysandres and sylvains comments
* final corrections on examples
* small fix for reformer
* Replace swish with silu
* revert nn.silu to nn.swish due to older version
* simplify optimized silu conditional and fix format
* Update activations.py
* Update activations_tf.py
* Update modeling_flax_utils.py
* Update modeling_openai.py
* add swish testcase
* add pytorch swish testcase
* Add more robust python version check
* more formatting fixes
Co-authored-by: TFUsers <TFUsers@gmail.com>
* Test TF GPU CI
* Change cache
* Fix missing torch requirement
* Fix some model tests
Style
* LXMERT
* MobileBERT
* Longformer skip test
* XLNet
* The rest of the tests
* RAG goes OOM in multi gpu setup
* YAML test files
* Last fixes
* Skip doctests
* Fill mask tests
* Yaml files
* Last test fix
* Style
* Update cache
* Change ONNX tests to slow + use tiny model
* move the helper code into testing_utils
* port test_trainer_distributed to work with pytest
* improve docs
* simplify notes
* doc
* doc
* style
* doc
* further improvements
* torch might not be available
* real fix
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* better reports
* a whole bunch of reports in their own files
* clean up
* improvements
* github artifacts experiment
* style
* complete the report generator with multiple improvements/fixes
* fix
* save all reports under one dir to easy upload
* can remove temp failing tests
* doc fix
* some cleanup
* WIP refactoring pipeline tests - switching to fast tokenizers
* fix dialog pipeline and fill-mask
* refactoring pipeline tests backbone
* make large tests slow
* fix tests (tf Bart inactive for now)
* fix doc...
* clean up for merge
* fixing tests - remove bart from summarization until there is TF
* fix quality and RAG
* Add new translation pipeline tests - fix JAX tests
* only slow for dialog
* Fixing the missing TF-BART imports in modeling_tf_auto
* spin out pipeline tests in separate CI job
* adding pipeline test to CI YAML
* add slow pipeline tests
* speed up tf and pt join test to avoid redoing all the standalone pt and tf tests
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
* Update src/transformers/pipelines.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/pipelines.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Update src/transformers/testing_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add require_torch and require_tf in is_pt_tf_cross_test
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Start simplification
* More progress
* Finished script
* Address comments and update tests instructions
* Wrong test
* Accept files as inputs and fix test
* Update src/transformers/trainer_utils.py
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
* Fix labels and add combined score
* Add special labels
* Update TPU command
* Revert to old label strategy
* Use model labels
* Fix for STT-B
* Styling
* Apply suggestions from code review
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
* Code styling
* Fix review comments
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
* Actually make the "translation", "translation_XX_to_YY" task behave correctly.
Background:
- Currently "translation_cn_to_ar" does not work. (only 3 pairs are
supported)
- Some models, contain in their config the correct values for the (src,
tgt) pair they can translate. It's usually just one pair, and we can
infer it automatically from the `model.config.task_specific_params`. If
it's not defined we can still probably load the TranslationPipeline
nevertheless.
Proposed fix:
- A simplified version of what could become more general which is
a `parametrized` task. "translation" + (src, tgt) in this instance
it what we need in the general case. The way we go about it for now
is simply parsing "translation_XX_to_YY". If cases of parametrized task arise
we should preferably go in something closer to what `datasets` propose
which is having a secondary argument `task_options`? that will be close
to what that task requires.
- Should be backward compatible in all cases for instance
`pipeline(task="translation_en_to_de") should work out of the box.
- Should provide a warning when a specific translation pair has been
selected on behalf of the user using
`model.config.task_specific_params`.
* Update src/transformers/pipelines.py
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
* fix config save
* add test
* add config class variable and another test
* line break
* fix fsmt and typo
* god am I making many errors today :-/
* Update src/transformers/configuration_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* slow tests should be slow
* exception note
* style
* integrate LysandreJik's notes with some expansions
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* another slow test
* fix link, and prose
* clarify.
* note from Sam
* typo
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* make the save_load special key tests common
* handle mbart
* cleaner solution
* fix
* move test_save_load_missing_keys back into fstm for now
* restore
* style
* add marian
* add pegasus
* blenderbot
* revert - no static embed
Sylvain Gugger