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ModelScope/data/test/regression/sbert-base-tnews.bin

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[to #42322933] Fix bug in UT daily 1. Fix bugs in daily test 2. Fix a bug that the updating of lr is before the first time of updating of optimizer TODO this will still cause warnings when GA is above 1 3. Remove the judgement of mode in text-classification's preprocessor to fit the base trainer(Bug) Update some regression bins to fit the preprocessor 4. Update the regression tool to let outer code modify atol and rtol 5. Add the default metric for text-classification task 6. Remove the useless ckpt conversion method in bert to avoid the requirement of tf when loading modeling_bert Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10430764
2 years ago
[to #42322933] NLP 1030 Refactor 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
2 years ago
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