feat: Implement TRPG NER training and inference script with robust model path detection and enhanced timestamp/speaker handling

- 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.

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