base-model/.github/workflows, branch v0.1.4 BaseModel for HydroRoll https://git.hydroroll.team/base-model/atom?h=v0.1.4 2025-12-30T12:54:06Z refactor: Clean up publish workflow by removing Test PyPI steps and improving artifact packaging 2025-12-30T12:54:06Z HsiangNianian i@jyunko.cn 2025-12-30T12:54:06Z urn:sha1:18c946aac2b0e16ec4e66bb4c40c62403af6f205 feat: Enhance model download functionality to support zip file retrieval and extraction 2025-12-30T12:49:12Z HsiangNianian i@jyunko.cn 2025-12-30T12:49:12Z urn:sha1:a4dd04f6e3af86ce3f96c7f7ebc88e195db366f4 refactor: Update model download functionality and improve inference module to support automatic model retrieval from GitHub releases 2025-12-30T12:39:34Z HsiangNianian i@jyunko.cn 2025-12-30T12:39:34Z urn:sha1:298035052b3e3d083b57f5dbac0e86de4f94efba fix: Update command to check distribution in publish workflow 2025-12-30T12:18:07Z HsiangNianian i@jyunko.cn 2025-12-30T12:18:07Z urn:sha1:eeecb738fccf2f541f98414f51e28ecf0bd710a3 refactor: Refactor TRPG NER model SDK: restructure codebase into base_model_trpgner package, implement training and inference modules, and add model download functionality. Remove legacy training and utils modules. Enhance documentation and examples for better usability. 2025-12-30T12:16:05Z HsiangNianian i@jyunko.cn 2025-12-30T12:16:05Z urn:sha1:5dd166366b8a2f4699c1841ebd7fceabcd9868a4 feat: Refactor and enhance TRPG NER model SDK 2025-12-30T11:54:08Z HsiangNianian i@jyunko.cn 2025-12-30T11:54:08Z urn: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.