modelscope
/
ModelScope
Not watched
1
0
Fork 0
Code
Releases 0 Wiki evaluate Activity
Issues 0 Pull Requests 0 Datasets Model Cloudbrain HPC
You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.
986 Commits
1 Branch
10 MB
55 Download
Tree: acb8d36699
Branches Tags
${ item.name }
Create branch ${ searchTerm }
from 'acb8d36699'
${ noResults }
ModelScope/data/test/regression/sbert_ws_en.bin

sbert_ws_en.bin 130 B
Raw Normal View History

[to #42322933] add regress tests Add regression test for some unit tests. Firstly, Run a baseline test to create a pickle file which contains the inputs and outputs of modules, then changes can be observed between the latest version and the baseline file. Some baseline files are submitted in the data/test/regression folder Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9814693
3 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
3 years ago
​
 123 
  1. version https://git-lfs.github.com/spec/v1

  2. oid sha256:cc6de82a8485fbfa008f6c2d5411cd07ba03e4a780bcb4e67efc6fba3c6ce92f

  3. size 63597

致力于通过开放的社区合作，开源AI模型以及相关创新技术，推动基于模型即服务的生态繁荣发展