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--- |
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datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- UniHGKR-base |
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widget: [] |
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--- |
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# UniHGKR-base |
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Our paper: [UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers](https://arxiv.org/abs/2410.20163). |
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Please see github repository [UniHGKR](https://github.com/ZhishanQ/UniHGKR/tree/main/code_for_UniHGKR_base) to know how to use this model. |
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We recommend using the [sentence-transformers](https://www.SBERT.net) package to load our model and to perform embedding for paragraphs and sentences. |
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It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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## Training Details |
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### Framework Versions |
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- Python: 3.8.10 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.44.2 |
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- PyTorch: 2.0.0+cu118 |
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- Accelerate: 0.34.0 |
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- Datasets: 2.21.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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If you find this resource useful in your research, please consider giving a like and citation. |
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``` |
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@article{min2024unihgkr, |
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title={UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers}, |
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author={Min, Dehai and Xu, Zhiyang and Qi, Guilin and Huang, Lifu and You, Chenyu}, |
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journal={arXiv preprint arXiv:2410.20163}, |
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year={2024} |
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} |
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``` |
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