base-model/.gitignore, branch v0.1.10BaseModel for HydroRoll https://git.hydroroll.team/base-model/atom?h=v0.1.102025-12-30T13:37:20Zrefactor: Refactor code structure for improved readability and maintainability2025-12-30T13:37:20ZHsiangNianiani@jyunko.cn2025-12-30T13:37:20Zurn:sha1:200ae0b8131367a55572cbfca00fbcf19257b84dfeat: Update Python version requirement and add onnxscript dependency2025-12-30T13:22:07ZHsiangNianiani@jyunko.cn2025-12-30T13:21:59Zurn:sha1:d35712d0f200b7862450b173a1bee95d1bd85dc8
- Changed the required Python version from >=3.12 to >=3.10 in pyproject.toml.
- Reformatted the dependencies section for better readability.
- Added "onnxscript>=0.5.7" to the train optional dependencies.
refactor: Refactor code structure for improved readability and maintainability2025-12-30T12:24:05ZHsiangNianiani@jyunko.cn2025-12-30T12:24:05Zurn:sha1:92a647ffbb3452a0ed49601177f290e20a88413efeat: Refactor and enhance TRPG NER model SDK2025-12-30T11:54:08ZHsiangNianiani@jyunko.cn2025-12-30T11:54:08Zurn:sha1:575114661ef9afb95df2a211e1d8498686340e6b
- Removed deprecated `word_conll_to_char_conll.py` utility and integrated its functionality into the new `utils` module.
- Introduced a comprehensive GitHub Actions workflow for automated publishing to PyPI and GitHub Releases.
- Added `__init__.py` files to establish package structure for `basemodel`, `inference`, `training`, and `utils` modules.
- Implemented model downloading functionality in `download_model.py` to fetch pre-trained ONNX models.
- Developed `TRPGParser` class for ONNX-based inference, including methods for parsing TRPG logs.
- Created training utilities in `training/__init__.py` for NER model training with Hugging Face Transformers.
- Enhanced utility functions for CoNLL file parsing and dataset creation.
- Added command-line interface for converting CoNLL files to datasets with validation options.
feat: Implement TRPG NER training and inference script with robust model path detection and enhanced timestamp/speaker handling2025-12-30T11:14:39ZHsiangNianiani@jyunko.cn2025-12-30T11:14:39Zurn:sha1:7ac684f1f82023c6284cd7d7efde11b8dc98c149
- Added main training and inference logic in main.py, including CoNLL parsing, tokenization, and model training.
- Introduced TRPGParser class for inference with entity aggregation and special handling for timestamps and speakers.
- Developed utility functions for converting word-level CoNLL to char-level and saving datasets in various formats.
- Added ONNX export functionality for the trained model.
- Created a comprehensive requirements.txt and updated pyproject.toml with necessary dependencies.
- Implemented tests for ONNX inference to validate model outputs.
refactor: Refactor code structure for improved readability and maintainability2025-12-29T11:59:36ZHsiangNianiani@jyunko.cn2025-12-29T11:59:36Zurn:sha1:12910f3a937633a25aa0de463a6edf756f2b8cddInitial commit2024-02-20T01:49:34Z简律纯i@jyunko.cn2024-02-20T01:49:34Zurn:sha1:8d7692731f5c0963db7c848349a6fd4b9ba8ab26