* Chunkable classification pipeline
The TokenClassificationPipeline is now able to process sequences longer than 512. No matter the framework, the model, the tokenizer. We just have to pass process_all=True and a stride number (optional). The behavior remains the same if you don't pass these optional parameters. For overlapping parts when using stride above 0, we consider only the max scores for each overlapped token in all chunks where the token is.
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* update with latest black format
* update black format
* Update token_classification.py
* Update token_classification.py
* format correction
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update comments
* Update src/transformers/pipelines/token_classification.py
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* Update token_classification.py
Correct spaces, remove process_all and keep only stride. If stride is provided, the pipeline is applied to the whole text.
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update chunk aggregation
Update the chunk aggregation strategy based on entities aggregation.
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
Remove unnecessary pop from outputs dict
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update src/transformers/pipelines/token_classification.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add chunking tests
* correct formating
* correct formatting
* correct model id for test chunking
* update scores with nested simplify
* Update test_pipelines_token_classification.py
* Update test_pipelines_token_classification.py
* update model to a tiny one
* Update test_pipelines_token_classification.py
* Adding smaller test for chunking.
* Fixup
* Update token_classification.py
* Update src/transformers/pipelines/token_classification.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/pipelines/token_classification.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [WIP] whisper refacto to support language output.
* Handling merges.
* A bit more cleanup and comments.
* Many improvements.
Lots of details everywhere.
* Cleanup old code and tests.
* Handle lone timestamp tokens (just recover when something bad happens).
* Adding return_language example.
* No ffmpeg.
* Hmm.
* Some corrections.
* Both fast and slow.
* New black.
* Update src/transformers/models/whisper/tokenization_whisper.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/whisper/tokenization_whisper.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Remove print.
* Undoing tests modifications.
* Smaller test modifications.
* Rename.
* Remove maxDiff.
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Mark pipeline tests to skip them easily
* Mark the mixin as pipeline test
* Update src/transformers/testing_utils.py
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
---------
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* add pipeline
* update init
* add zero shot to init
* update inits and correct checkpoints
* update base to support input features
* add tests
* Update src/transformers/pipelines/zero_shot_audio_classification.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Update src/transformers/pipelines/zero_shot_audio_classification.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* update pieline code
* use tiny checkpoint
* nits and expected value with tiny model
* style
* last nit on tests values
* fix styling
* fix collate fn that was casting t float
* update
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* fix: Change is_last chunk calc and add conditional break
* format fix
* account for 0 and full stride_rights, add comment
* add new test
* make style
* update slow whisper asr test timestamps
* use nested_simplify on output and round timestamp to hundreths place
* Result of black 23.1
* Update target to Python 3.7
* Switch flake8 to ruff
* Configure isort
* Configure isort
* Apply isort with line limit
* Put the right black version
* adapt black in check copies
* Fix copies
* update whisper logit processor
* add generate for whisper
* remove part of the whisper specific code from pipeline
* update logit processes
* major update
* enforce first timestamp
* update generate
* add more tests
* update new decoding strategy
* Apply suggestions from code review
* update docstring
* fixup
* default config will not have multilingual ar
* update expected tokenizer size, see pull on the hub for whisper-tiny
* Fixing the pipeline with image processor.
* Update the slow test.
* Using only the first image processor.
* Include exclusion mecanism for Image processor.
* Do not handle Gitconfig, deemed as a bug.
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove `conversational` changes. They are not supposed to be here.
* Address first row of comments.
* Remove OneFormer modifications.
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add draft logit processor
* add template functions
* update timesapmt processor parameters
* draft script
* simplify code
* cleanup
* fixup and clean
* update pipeline
* style
* clean up previous idea
* add tokenization utils
* update tokenizer and asr output
* fit whisper type
* style and update test
* clean test
* style test
* update tests
* update error test
* udpate code (not based on review yet)
* update tokenization
* update asr pipeline
* update code
* cleanup and update test
* fmt
* remove text verificatino
* cleanup
* cleanup
* add model test
* update tests
* update code add docstring
* update code and add docstring
* fix pipeline tests
* add draft logit processor
add template functions
update timesapmt processor parameters
draft script
simplify code
cleanup
fixup and clean
update pipeline
style
clean up previous idea
add tokenization utils
update tokenizer and asr output
fit whisper type
style and update test
clean test
style test
update tests
update error test
udpate code (not based on review yet)
update tokenization
update asr pipeline
update code
cleanup and update test
fmt
remove text verificatino
cleanup
cleanup
add model test
update tests
update code add docstring
update code and add docstring
fix pipeline tests
* Small update.
* Fixup.
* Tmp.
* More support.
* Making `forced_decoder_ids` non mandatory for users to set.
* update and fix first bug
* properly process sequence right after merge if last
* tofo
* allow list inputs + compute begin index better
* start adding tests
* add the 3 edge cases
* style
* format sequences
* fixup
* update
* update
* style
* test passes, edge cases should be good
* update last value
* remove Trie
* update tests and expec ted values
* handle bigger chunk_length
* clean tests a bit
* refactor chunk iter and clean pipeline
* update tests
* style
* refactor chunk iter and clean pipeline
* upade
* resolve comments
* Apply suggestions from code review
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* take stride right into account
* update test expected values
* Update code based on review
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* major refactor
* add correct strides for tests
* Update src/transformers/pipelines/automatic_speech_recognition.py
* fix whisper timestamp test
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* add draft logit processor
* add template functions
* update timesapmt processor parameters
* draft script
* simplify code
* cleanup
* fixup and clean
* update pipeline
* style
* clean up previous idea
* add tokenization utils
* update tokenizer and asr output
* fit whisper type
* style and update test
* clean test
* style test
* update tests
* update error test
* udpate code (not based on review yet)
* update tokenization
* update asr pipeline
* update code
* cleanup and update test
* fmt
* remove text verificatino
* cleanup
* cleanup
* add model test
* update tests
* update code add docstring
* update code and add docstring
* fix pipeline tests
* add draft logit processor
add template functions
update timesapmt processor parameters
draft script
simplify code
cleanup
fixup and clean
update pipeline
style
clean up previous idea
add tokenization utils
update tokenizer and asr output
fit whisper type
style and update test
clean test
style test
update tests
update error test
udpate code (not based on review yet)
update tokenization
update asr pipeline
update code
cleanup and update test
fmt
remove text verificatino
cleanup
cleanup
add model test
update tests
update code add docstring
update code and add docstring
fix pipeline tests
* Small update.
* Fixup.
* Tmp.
* More support.
* Making `forced_decoder_ids` non mandatory for users to set.
* update and fix first bug
* properly process sequence right after merge if last
* tofo
* allow list inputs + compute begin index better
* start adding tests
* add the 3 edge cases
* style
* format sequences
* fixup
* update
* update
* style
* test passes, edge cases should be good
* update last value
* remove Trie
* update tests and expec ted values
* handle bigger chunk_length
* clean tests a bit
* refactor chunk iter and clean pipeline
* update tests
* style
* refactor chunk iter and clean pipeline
* upade
* resolve comments
* Apply suggestions from code review
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* take stride right into account
* update test expected values
* Update code based on review
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* Fixing #20783
* Update src/transformers/pipelines/base.py
* Fixing some tests.
* Fixup.
* Remove ffmpeg dep + a bit more relaxed for bigbird QA precision.
* Better dataset.
* Prevent failing on TF.
* Better condition. We can't use `can_use_iterator` since we cannot use it
directly.
* add torch_dtype attribute to Pipeline
* Use torch_dtype to cast input tensor type in AutomaticSpeechRecognitionPipeline
* Fix code quality
* Add TextGenerationPipeline fp16 test
* Fix code quality
* Remove useless require in tests
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* Supporting `fp16` for asr pipeline
* Adding test.
* Style.
* Oops.
* Flake8 update ?
* Fixing flake8 ?
* Revert "Flake8 update ?"
This reverts commit 0b917fcb52.
* Style (acctidentally deleted flake8 F401.)
* Move to a bigger test (no small whisper model, and s2t doesn't seem to
accept torch_dtype=fp16).
Also we need to use a GPU to actually compute on fp16.
* Using BatchFeature capability.
* Copy RoBERTa
* formatting
* implement RoBERTa with prelayer normalization
* update test expectations
* add documentation
* add convertion script for DinkyTrain weights
* update checkpoint repo
Unfortunately the original checkpoints assumes a hacked roberta model
* add to RoBERTa-PreLayerNorm docs to toc
* run utils/check_copies.py
* lint files
* remove unused import
* fix check_repo reporting wrongly a test is missing
* fix import error, caused by rebase
* run make fix-copies
* add RobertaPreLayerNormConfig to ROBERTA_EMBEDDING_ADJUSMENT_CONFIGS
* Fix documentation <Facebook> -> Facebook
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fixup: Fix documentation <Facebook> -> Facebook
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add missing Flax header
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* expected_slice -> EXPECTED_SLICE
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update copies after rebase
* add missing copied from statements
* make fix-copies
* make prelayernorm explicit in code
* fix checkpoint path for the original implementation
* add flax integration tests
* improve docs
* update utils/documentation_tests.txt
* lint files
* Remove Copyright notice
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* make fix-copies
* Remove EXPECTED_SLICE calculation comments
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
