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--- |
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license: mit |
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tags: |
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- graph-neural-networks |
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- gnn |
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- mathematical-proofs |
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- curriculum-learning |
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- pytorch |
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language: |
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- en |
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library_name: pytorch |
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--- |
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# Merge2Docs Models |
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This repository contains pre-trained models for the [Merge2Docs](https://github.com/pechang03/merge2docs) project - a research project combining LLMs with advanced graph algorithms for sophisticated document merging. |
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## Models |
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Total size: **4.38 MB** |
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### sparse_hierarchical_net_v1.pth |
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- **Type**: GNN |
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- **Size**: 0.375 MB |
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- **Description**: Sparse Hierarchical GNN model for graph neural network tasks |
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- **Path**: `sparse_hierarchical_net_v1.pth` |
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### curriculum_ultimate_model.pth |
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- **Type**: Math/Curriculum |
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- **Size**: 4.0 MB |
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- **Description**: Curriculum learning model for mathematical proof routing |
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- **Path**: `curriculum_ultimate_model.pth` |
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## Usage |
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### Download models |
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```python |
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from huggingface_hub import hf_hub_download |
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# Download GNN model |
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gnn_model_path = hf_hub_download( |
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repo_id="peterliu06/merge2docs-models", |
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filename="sparse_hierarchical_net_v1.pth" |
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) |
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# Download curriculum model |
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math_model_path = hf_hub_download( |
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repo_id="peterliu06/merge2docs-models", |
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filename="curriculum_ultimate_model.pth" |
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) |
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``` |
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### Load in PyTorch |
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```python |
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import torch |
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# Load GNN model |
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gnn_model = torch.load(gnn_model_path) |
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# Load curriculum model |
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math_model = torch.load(math_model_path) |
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``` |
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## Model Details |
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### Sparse Hierarchical GNN (sparse_hierarchical_net_v1.pth) |
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A Graph Neural Network designed for hierarchical document structure analysis. |
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**Architecture:** |
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- Sparse graph representation |
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- Hierarchical message passing |
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- Optimized for document-level relationships |
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**Training:** |
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- Dataset: Document graph structures |
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- Framework: PyTorch + PyTorch Geometric |
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- Hardware: GPU-accelerated training |
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### Curriculum Ultimate Model (curriculum_ultimate_model.pth) |
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A curriculum learning-based model for mathematical proof routing and validation. |
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**Architecture:** |
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- Progressive difficulty learning |
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- Multi-task proof validation |
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- Curriculum-based training strategy |
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**Training:** |
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- Dataset: Mathematical proofs and reasoning tasks |
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- Framework: PyTorch |
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- Approach: Curriculum learning with increasing complexity |
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## Integration with Merge2Docs |
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To use these models with Merge2Docs, update your `.env_m2d`: |
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```bash |
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# Download from Hugging Face |
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GNN_MODEL_PATH="./models/sparse_hierarchical_net_v1.pth" |
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MATH_MODEL_PATH="./models/curriculum_ultimate_model.pth" |
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``` |
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Then download the models: |
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```bash |
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cd merge2docs |
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python scripts/download_models_from_huggingface.py |
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``` |
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## Citation |
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If you use these models in your research, please cite: |
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```bibtex |
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@software{merge2docs_models, |
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author = {Chang, Peter}, |
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title = {Merge2Docs Pre-trained Models}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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url = {https://huggingface.co/peterliu06/merge2docs-models} |
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} |
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``` |
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## License |
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MIT License - See [LICENSE](https://github.com/pechang03/merge2docs/blob/main/LICENSE) |
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## Project Links |
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- **GitHub**: https://github.com/pechang03/merge2docs |
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- **Documentation**: See project README |
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- **Issues**: https://github.com/pechang03/merge2docs/issues |
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## Model Updates |
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Models are periodically retrained and updated. Check the git history for version information. |
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