* fix wrong method name tf.concatenate
* add tests related to causal LM / decoder
* make style and quality
* clean-up
* Fix TFBertModel's extended_attention_mask when past_key_values is provided
* Fix tests
* fix copies
* More tf.int8 -> tf.int32 in TF test template
* clean-up
* Update TF test template
* revert the previous commit + update the TF test template
* Fix TF template extended_attention_mask when past_key_values is provided
* Fix some styles manually
* clean-up
* Fix ValueError: too many values to unpack in the test
* Fix more: too many values to unpack in the test
* Add a comment for extended_attention_mask when there is past_key_values
* Fix TFElectra extended_attention_mask when past_key_values is provided
* Add tests to other TF models
* Fix for TF Electra test: add prepare_config_and_inputs_for_decoder
* Fix not passing training arg to lm_head in TFRobertaForCausalLM
* Fix tests (with past) for TF Roberta
* add testing for pask_key_values for TFElectra model
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Adding the option to return_timestamps on pure CTC ASR models.
* Remove `math.prod` which was introduced in Python 3.8
* int are not floats.
* Reworking the PR to support "char" vs "word" output.
* Fixup!
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Quality.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* custom_models: tiny doc addition
* mention security feature earlier in the section
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Enabling Beit SegFormer to `image-segmentation`.
* Fixing the score.
* Fix import ?
* Missing in type hint.
* Multiple test fixes:
- Add `raw_image` support. It should be the default IMHO since in Python
world it doesn't make any sense to base64 encode the image (Sorry
@mishig, didn't catch that in my review). I really think we should
consider breaking BC here.
- Add support for Segformer tiny test (needed
`SegformerModelTester.get_config` to enable TinyConfig
@NielsRogge)
- Add the check that `batch_size` works correctly on that pipeline.
Uncovered that it doesn't for Detr, which IMO is OK since images
after `feature_extractor` don't have the same size. Comment should
explain.
* Type hint as a string.
* Make fixup + update black.
* torch+vision protections.
* Don't use torchvision, use F.interpolate instead (no new dep).
* Last fixes for Segformer.
* Update test to reflect new image (which was broken)
* Update tests.
* Major BC modification:
- Removed the string compressed PNG string, that's a job for users
`transformers` stays in python land.
- Removed the `score` for semantic segmentation. It has hardly a meaning
on its own in this context.
- Don't include the grayscale with logits for now (which could enable
users to get a sense of confidence). Might be done later.
- Don't include the surface of the mask (could be used for sorting by
users, to filter out small masks). It's already calculable, and
it's easier to add later, than to add now and break later if we need.
* `make fixup`.
* Small changes.
* Rebase + doc fixup.
* [Proposal] Adding ZeroShotImageClassificationPipeline
- Based on CLIP
* WIP, Resurection in progress.
* Resurrection... achieved.
* Reword handling different `padding_value` for `feature_extractor` and
`tokenizer`.
* Thanks doc-builder !
* Adding docs + global namespace `ZeroShotImageClassificationPipeline`.
* Fixing templates.
* Make the test pass and be robust to floating error.
* Adressing suraj's comments on docs mostly.
* Tf support start.
* TF support.
* Update src/transformers/pipelines/zero_shot_image_classification.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* begin script
* update script
* fix features and data args
* main
* add requirements
* add column name args
* fix captions
* don't jit transforms
* fix caption
* fix labels, handle attention mask
* convert pixel values to numpy
* labels => input_ids
* transform images on the fly
* use AutoModel class, create the hybird model outside of the script
* fix version message
* add readme
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* adderss review comments
* add more comments
* allow freezing vision and text models
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
