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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 |
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widget: [] |
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--- |
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# UniHGKR-base-beir |
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Our paper: [UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers](https://arxiv.org/abs/2410.20163). |
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The UniHGKR-base-beir model is derived from the UniHGKR-base model, further fine-tuned on MS MARCO for evaluation on the BEIR benchmark. 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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## Evaluation on BEIR |
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The evaluation code can be found at https://github.com/ZhishanQ/UniHGKR. |
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## Model Details |
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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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## Usage |
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Use the instructions to achieve the best performance from the model: |
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``` |
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general_ins = "Given a question, retrieve relevant evidence that can answer the question from all knowledge sources:" |
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single_source_inst = "Given a question, retrieve relevant evidence that can answer the question from Text sources:" |
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``` |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("ZhishanQ/UniHGKR-base-beir") |
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# Run inference |
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general_ins = "Given a question, retrieve relevant evidence that can answer the question from all knowledge sources:" |
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single_source_inst = "Given a question, retrieve relevant evidence that can answer the question from Text sources:" |
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sentences = [ |
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'The weather is lovely today.', |
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"It's so sunny outside!", |
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'He drove to the stadium.', |
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] |
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# Prepend each sentence with the instruction |
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updated_sentences = [f"{single_source_inst} {sentence}" for sentence in sentences] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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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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### Sentence Transformers Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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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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