base-model - Base Model (GitHub mirror)

	Commit message (Collapse)	Author	Age	Files	Lines
*	feat: Refine model packaging process to include only necessary inference files	HsiangNianian	2025-12-30	1	-2/+8
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*	fix: Simplify artifact upload paths in GitHub release workflow	HsiangNianian	2025-12-30	1	-4/+2
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*	feat: Add debug step for artifact structure and fix model zip path in release	HsiangNianian	2025-12-30	1	-2/+7
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*	refactor: Clean up publish workflow by removing Test PyPI steps and ↵	HsiangNianian	2025-12-30	1	-88/+15
\| \| \| \|	improving artifact packaging
*	feat: Enhance model download functionality to support zip file retrieval and ↵	HsiangNianian	2025-12-30	1	-2/+31
\| \| \| \|	extraction
*	refactor: Update model download functionality and improve inference module ↵	HsiangNianian	2025-12-30	1	-3/+1
\| \| \| \|	to support automatic model retrieval from GitHub releases
*	fix: Update command to check distribution in publish workflow	HsiangNianian	2025-12-30	1	-1/+1
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*	refactor: Refactor TRPG NER model SDK: restructure codebase into ↵	HsiangNianian	2025-12-30	1	-21/+75
\| \| \| \|	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.
*	feat: Refactor and enhance TRPG NER model SDK	HsiangNianian	2025-12-30	1	-0/+201
	- 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.