* New TF version compatibility fixes
* Remove dummy print statement, move expand_1d
* Make a proper framework inference function
* Make a proper framework inference function
* ValueError -> TypeError
* initial work
* Add other classes
* Refactor code
* Move warning and fix dynamic pipeline
* Issue warning when necessary
* Add test
* Do not skip auto tests
* Fix failing tests
* Refactor and address review comments
* Address review comments
* 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>
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Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Make ZeroShotImageClassificationPipeline faster
The pipeline makes separate calls to model for each candidate label.
This commit combines all labels into one call.
Original code takes more that 60 seconds to process one image and 1000
candidate labels. Updated code takes less than 2 seconds.
* implement batching
* code formatting
* Creating an even faster zero-shot-image-classifiction.
Unfortunately super tailored towards CLIP.
Co-Authored-By: Yessen Kanapin <yessen@deepinfra.com>
* Quality.
* Cleanup.
* Order different on the CI it seems.
* Cleanup.
* Quality.
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Co-authored-by: Yessen Kanapin <yessen@deepinfra.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.
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Co-authored-by: Arthur <48595927+ArthurZucker@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
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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>
