* 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
* 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
* 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>
* Updated the Extractive Question Answering code snippets
The Extractive Question Answering code snippets do not work anymore since the models return task-specific output objects. This commit fixes the pytorch and tensorflow examples but adding `.values()` to the model call.
* Update task_summary.rst
* 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>
* <small>tiny typo</small>
* Tokenizers: ability to load from model subfolder
* use subfolder for local files as well
* Uniformize model shortcut name => model id
* from s3 => from huggingface.co
Co-authored-by: Quentin Lhoest <lhoest.q@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
* First addition of Flax/Jax documentation
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* make style
* Ensure input order match between Bert & Roberta
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Install dependencies "all" when building doc
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* wraps build_doc deps with ""
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing @sgugger comments.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Use list to highlight JAX features.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Make style.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Let's not look to much into the future for now.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Style
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* 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>
* [testing utils] get_auto_remove_tmp_dir default change
Now that I have been using `get_auto_remove_tmp_dir default change` for a while, I realized that the defaults aren't most optimal.
99% of the time we want the tmp dir to be empty at the beginning of the test - so changing the default to `before=True` - this shouldn't impact any tests since this feature is used only during debug.
* simplify things
* update docs
* fix doc layout
* style
* Update src/transformers/testing_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* better 3-state doc
* style
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* s/tmp/temporary/ + style
* correct the statement
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.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
* 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>
* 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>
* 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
* 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>
* first attempt to add AzureML callbacks
* func arg fix
* var name fix, but still won't fix error...
* fixing as in https://discuss.huggingface.co/t/how-to-integrate-an-azuremlcallback-for-logging-in-azure/1713/2
* Avoid lint check of azureml import
* black compliance
* Make isort happy
* Fix point typo in docs
* Add AzureML to Callbacks docs
* Attempt to make sphinx happy
* Format callback docs
* Make documentation style happy
* Make docs compliant to style
Co-authored-by: Davide Fiocco <davide.fiocco@frontiersin.net>
* Important files
* Styling them all
* Revert "Styling them all"
This reverts commit 7d029395fd.
* Syling them for realsies
* Fix syntax error
* Fix benchmark_utils
* More fixes
* Fix modeling auto and script
* Remove new line
* Fixes
* More fixes
* Fix more files
* Style
* Add FSMT
* More fixes
* More fixes
* More fixes
* More fixes
* Fixes
* More fixes
* More fixes
* Last fixes
* Make sphinx happy
* Fix minor typos
Fix minor typos in the docs.
* Update docs/source/preprocessing.rst
Clearer data structure description.
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add MLflow integration class
Add integration code for MLflow in integrations.py along with the code
that checks that MLflow is installed.
* Add MLflowCallback import
Add import of MLflowCallback in trainer.py
* Handle model argument
Allow the callback to handle model argument and store model config items as hyperparameters.
* Log parameters to MLflow in batches
MLflow cannot log more than a hundred parameters at once.
Code added to split the parameters into batches of 100 items and log the batches one by one.
* Fix style
* Add docs on MLflow callback
* Fix issue with unfinished runs
The "fluent" api used in MLflow integration allows only one run to be active at any given moment. If the Trainer is disposed off and a new one is created, but the training is not finished, it will refuse to log the results when the next trainer is created.
* Add MLflow integration class
Add integration code for MLflow in integrations.py along with the code
that checks that MLflow is installed.
* Add MLflowCallback import
Add import of MLflowCallback in trainer.py
* Handle model argument
Allow the callback to handle model argument and store model config items as hyperparameters.
* Log parameters to MLflow in batches
MLflow cannot log more than a hundred parameters at once.
Code added to split the parameters into batches of 100 items and log the batches one by one.
* Fix style
* Add docs on MLflow callback
* Fix issue with unfinished runs
The "fluent" api used in MLflow integration allows only one run to be active at any given moment. If the Trainer is disposed off and a new one is created, but the training is not finished, it will refuse to log the results when the next trainer is created.
* 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>
* add CustomHFIndex
* typo in config
* update tests
* add custom dataset example
* clean script
* update test data
* minor in test
* docs
* docs
* style
* fix imports
* allow to pass the indexed dataset directly
* update tests
* use multiset DPR
* address thom and patrick's comments
* style
* update dpr tokenizer
* add output_dir flag in use_own_knowledge_dataset.py
* allow custom datasets in examples/rag/finetune.py
* add test for custom dataset in distributed rag retriever
* splitting fast and slow tokenizers [WIP]
* [WIP] splitting sentencepiece and tokenizers dependencies
* update dummy objects
* add name_or_path to models and tokenizers
* prefix added to file names
* prefix
* styling + quality
* spliting all the tokenizer files - sorting sentencepiece based ones
* update tokenizer version up to 0.9.0
* remove hard dependency on sentencepiece 🎉
* and removed hard dependency on tokenizers 🎉
* update conversion script
* update missing models
* fixing tests
* move test_tokenization_fast to main tokenization tests - fix bugs
* bump up tokenizers
* fix bert_generation
* update ad fix several tokenizers
* keep sentencepiece in deps for now
* fix funnel and deberta tests
* fix fsmt
* fix marian tests
* fix layoutlm
* fix squeezebert and gpt2
* fix T5 tokenization
* fix xlnet tests
* style
* fix mbart
* bump up tokenizers to 0.9.2
* fix model tests
* fix tf models
* fix seq2seq examples
* fix tests without sentencepiece
* fix slow => fast conversion without sentencepiece
* update auto and bert generation tests
* fix mbart tests
* fix auto and common test without tokenizers
* fix tests without tokenizers
* clean up tests lighten up when tokenizers + sentencepiece are both off
* style quality and tests fixing
* add sentencepiece to doc/examples reqs
* leave sentencepiece on for now
* style quality split hebert and fix pegasus
* WIP Herbert fast
* add sample_text_no_unicode and fix hebert tokenization
* skip FSMT example test for now
* fix style
* fix fsmt in example tests
* update following Lysandre and Sylvain's comments
* Update src/transformers/testing_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/testing_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add Documentation for GPT-1 Classification
* Add GPT-1 with Classification head
* Add tests for GPT-1 Classification
* Add GPT-1 For Classification to auto models
* Remove authorized missing keys, change checkpoint to openai-gpt
* [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>
* 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>
* 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
* Clean up model documentation
* Formatting
* Preparation work
* Long lines
* Main work on rst files
* Cleanup all config files
* Syntax fix
* Clean all tokenizers
* Work on first models
* Models beginning
* FaluBERT
* All PyTorch models
* All models
* Long lines again
* Fixes
* More fixes
* Update docs/source/model_doc/bert.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Update docs/source/model_doc/electra.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Last fixes
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* skip decorators: docs, tests, bugs
* another important note
* style
* bloody style
* add @pytest.mark.parametrize
* add note
* no idea what it wants :(
* added rag WIP
* path fix
* Formatting / renaming prior to actual work
* added rag WIP
* path fix
* Formatting / renaming prior to actual work
* added rag WIP
* path fix
* Formatting / renaming prior to actual work
* added rag WIP
* Formatting / renaming prior to actual work
* First commit
* improve comments
* Retrieval evaluation scripts
* refactor to include modeling outputs + MPI retriever
* Fix rag-token model + refactor
* Various fixes + finetuning logic
* use_bos fix
* Retrieval refactor
* Finetuning refactoring and cleanup
* Add documentation and cleanup
* Remove set_up_rag_env.sh file
* Fix retrieval wit HF index
* Fix import errors
* Fix quality errors
* Refactor as per suggestions in https://github.com/huggingface/transformers/pull/6813#issuecomment-687208867
* fix quality
* Fix RAG Sequence generation
* minor cleanup plus initial tests
* fix test
* fix tests 2
* Comments fix
* post-merge fixes
* Improve readme + post-rebase refactor
* Extra dependencied for tests
* Fix tests
* Fix tests 2
* Refactor test requirements
* Fix tests 3
* Post-rebase refactor
* rename nlp->datasets
* RAG integration tests
* add tokenizer to slow integration test and allow retriever to run on cpu
* add tests; fix position ids warning
* change structure
* change structure
* add from encoder generator
* save working solution
* make all integration tests pass
* add RagTokenizer.save/from_pretrained and RagRetriever.save/from_pretrained
* don't save paths
* delete unnecessary imports
* pass config to AutoTokenizer.from_pretrained for Rag tokenizers
* init wiki_dpr only once
* hardcode legacy index and passages paths (todo: add the right urls)
* finalize config
* finalize retriver api and config api
* LegacyIndex index download refactor
* add dpr to autotokenizer
* make from pretrained more flexible
* fix ragfortokengeneration
* small name changes in tokenizer
* add labels to models
* change default index name
* add retrieval tests
* finish token generate
* align test with previous version and make all tests pass
* add tests
* finalize tests
* implement thoms suggestions
* add first version of test
* make first tests work
* make retriever platform agnostic
* naming
* style
* add legacy index URL
* docstrings + simple retrieval test for distributed
* clean model api
* add doc_ids to retriever's outputs
* fix retrieval tests
* finish model outputs
* finalize model api
* fix generate problem for rag
* fix generate for other modles
* fix some tests
* save intermediate
* set generate to default
* big refactor generate
* delete rag_api
* correct pip faiss install
* fix auto tokenization test
* fix faiss install
* fix test
* move the distributed logic to examples
* model page
* docs
* finish tests
* fix dependencies
* fix import in __init__
* Refactor eval_rag and finetune scripts
* start docstring
* add psutil to test
* fix tf test
* move require torch to top
* fix retrieval test
* align naming
* finish automodel
* fix repo consistency
* test ragtokenizer save/load
* add rag model output docs
* fix ragtokenizer save/load from pretrained
* fix tokenizer dir
* remove torch in retrieval
* fix docs
* fixe finetune scripts
* finish model docs
* finish docs
* remove auto model for now
* add require torch
* remove solved todos
* integrate sylvains suggestions
* sams comments
* correct mistake on purpose
* improve README
* Add generation test cases
* fix rag token
* clean token generate
* fix test
* add note to test
* fix attention mask
* add t5 test for rag
* Fix handling prefix in finetune.py
* don't overwrite index_name
Co-authored-by: Patrick Lewis <plewis@fb.com>
Co-authored-by: Aleksandra Piktus <piktus@devfair0141.h2.fair>
Co-authored-by: Aleksandra Piktus <piktus@learnfair5102.h2.fair>
Co-authored-by: Aleksandra Piktus <piktus@learnfair5067.h2.fair>
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Quentin Lhoest <lhoest.q@gmail.com>
* ready for PR
* cleanup
* correct FSMT_PRETRAINED_MODEL_ARCHIVE_LIST
* fix
* perfectionism
* revert change from another PR
* odd, already committed this one
* non-interactive upload workaround
* backup the failed experiment
* store langs in config
* workaround for localizing model path
* doc clean up as in https://github.com/huggingface/transformers/pull/6956
* style
* back out debug mode
* document: run_eval.py --num_beams 10
* remove unneeded constant
* typo
* re-use bart's Attention
* re-use EncoderLayer, DecoderLayer from bart
* refactor
* send to cuda and fp16
* cleanup
* revert (moved to another PR)
* better error message
* document run_eval --num_beams
* solve the problem of tokenizer finding the right files when model is local
* polish, remove hardcoded config
* add a note that the file is autogenerated to avoid losing changes
* prep for org change, remove unneeded code
* switch to model4.pt, update scores
* s/python/bash/
* missing init (but doesn't impact the finetuned model)
* cleanup
* major refactor (reuse-bart)
* new model, new expected weights
* cleanup
* cleanup
* full link
* fix model type
* merge porting notes
* style
* cleanup
* have to create a DecoderConfig object to handle vocab_size properly
* doc fix
* add note (not a public class)
* parametrize
* - add bleu scores integration tests
* skip test if sacrebleu is not installed
* cache heavy models/tokenizers
* some tweaks
* remove tokens that aren't used
* more purging
* simplify code
* switch to using decoder_start_token_id
* add doc
* Revert "major refactor (reuse-bart)"
This reverts commit 226dad15ca.
* decouple from bart
* remove unused code #1
* remove unused code #2
* remove unused code #3
* update instructions
* clean up
* move bleu eval to examples
* check import only once
* move data+gen script into files
* reuse via import
* take less space
* add prepare_seq2seq_batch (auto-tested)
* cleanup
* recode test to use json instead of yaml
* ignore keys not needed
* use the new -y in transformers-cli upload -y
* [xlm tok] config dict: fix str into int to match definition (#7034)
* [s2s] --eval_max_generate_length (#7018)
* Fix CI with change of name of nlp (#7054)
* nlp -> datasets
* More nlp -> datasets
* Woopsie
* More nlp -> datasets
* One last
* extending to support allen_nlp wmt models
- allow a specific checkpoint file to be passed
- more arg settings
- scripts for allen_nlp models
* sync with changes
* s/fsmt-wmt/wmt/ in model names
* s/fsmt-wmt/wmt/ in model names (p2)
* s/fsmt-wmt/wmt/ in model names (p3)
* switch to a better checkpoint
* typo
* make non-optional args such - adjust tests where possible or skip when there is no other choice
* consistency
* style
* adjust header
* cards moved (model rename)
* use best custom hparams
* update info
* remove old cards
* cleanup
* s/stas/facebook/
* update scores
* s/allen_nlp/allenai/
* url maps aren't needed
* typo
* move all the doc / build /eval generators to their own scripts
* cleanup
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* fix indent
* duplicated line
* style
* use the correct add_start_docstrings
* oops
* resizing can't be done with the core approach, due to 2 dicts
* check that the arg is a list
* style
* style
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>
* Initial model
* Fix upsampling
* Add special cls token id and test
* Formatting
* Test and fist FunnelTokenizerFast
* Common tests
* Fix the check_repo script and document Funnel
* Doc fixes
* Add all models
* Write doc
* Fix test
* Initial model
* Fix upsampling
* Add special cls token id and test
* Formatting
* Test and fist FunnelTokenizerFast
* Common tests
* Fix the check_repo script and document Funnel
* Doc fixes
* Add all models
* Write doc
* Fix test
* Fix copyright
* Forgot some layers can be repeated
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/modeling_funnel.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Address review comments
* Update src/transformers/modeling_funnel.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Address review comments
* Update src/transformers/modeling_funnel.py
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
* Slow integration test
* Make small integration test
* Formatting
* Add checkpoint and separate classification head
* Formatting
* Expand list, fix link and add in pretrained models
* Styling
* Add the model in all summaries
* Typo fixes
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
Tested in a local build of the docs.
e.g. Just above https://huggingface.co/transformers/task_summary.html#causal-language-modeling
Copy will copy the full code, e.g.
for token in top_5_tokens:
print(sequence.replace(tokenizer.mask_token, tokenizer.decode([token])))
Instead of currently only:
for token in top_5_tokens:
>>> for token in top_5_tokens:
... print(sequence.replace(tokenizer.mask_token, tokenizer.decode([token])))
Distilled models are smaller than the models they mimic. Using them instead of the large versions would help reduce our carbon footprint.
Distilled models are smaller than the models they mimic. Using them instead of the large versions would help increase our carbon footprint.
Distilled models are smaller than the models they mimic. Using them instead of the large versions would help decrease our carbon footprint.
Distilled models are smaller than the models they mimic. Using them instead of the large versions would help offset our carbon footprint.
Distilled models are smaller than the models they mimic. Using them instead of the large versions would help improve our carbon footprint.
Docs for the option fix:
https://sphinx-copybutton.readthedocs.io/en/latest/
