1. Exporting: Support text-classification of bert and tensorflow2.0 models, test cases have been added.
2. Downloading of preprocessor.from_pretrained will ignores some large files which not needed by extension file name.
3. Move sentence-piece-preprocessor to the subclass of text-generation-preprocessor and keep the original name for compatibility.
4. Remove some useless codes in nlp-trainer and trainer.
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11206922
Features:
1. Refactor the directory structure of nlp models. All model files are placed into either the model folder or the task_model folder
2. Refactor all the comments to google style
3. Add detail comments to important tasks and nlp models, to list the description of the model, and its preprocessor&trainer
4. Model Exporting now supports a direct all to TorchModelExporter(no need to derive from it)
5. Refactor model save_pretrained method to support direct running(independent from trainer)
6. Remove the judgement of Model in the pipeline base class, to support outer register models running in our pipelines
7. Nlp trainer now has a NLPTrainingArguments class , user can pass arguments into the dataclass, and use it as a normal cfg_modify_fn, to simplify the operation of modify cfg.
8. Merge the BACKBONES and the MODELS, so user can get a backbone with the Model.from_pretrained call
9. Model.from_pretrained now support a task argument, so user can use a backbone and load it with a specific task class.
10. Support Preprocessor.from_pretrained method
11. Add standard return classes to important nlp tasks, so some of the pipelines and the models are independent now, the return values of the models will always be tensors, and the pipelines will take care of the conversion to numpy and the following stuffs.
12. Split the file of the nlp preprocessors, to make the dir structure more clear.
Bugs Fixing:
1. Fix a bug that lr_scheduler can be called earlier than the optimizer's step
2. Fix a bug that the direct call of Pipelines (not from pipeline(xxx)) throws error
3. Fix a bug that the trainer will not call the correct TaskDataset class
4. Fix a bug that the internal loading of dataset will throws error in the trainer class
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10490585
1. Add exporter module
2. Move collate_fn out of the base pipeline class for reusing.
3. Add dummy inputs method in nlp tokenization preprocessor base class
4. Support Mapping in tensor numpify and detaching.
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10037704
