sentence-transformers safetensors bert sentence-similarity feature-extraction UniHGKR arxiv:2410.20163 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- UniHGKR
widget: []
---

# UniHGKR-base-beir

Our paper: [UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers](https://arxiv.org/abs/2410.20163).

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.

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.


## Evaluation on BEIR

The evaluation code can be found at https://github.com/ZhishanQ/UniHGKR.


## Model Details


### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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})
  (2): Normalize()
)
```

## Usage

Use the instructions to achieve the best performance from the model:
```
general_ins = "Given a question, retrieve relevant evidence that can answer the question from all knowledge sources:"
single_source_inst = "Given a question, retrieve relevant evidence that can answer the question from Text sources:"
```

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("ZhishanQ/UniHGKR-base-beir")
# Run inference

general_ins = "Given a question, retrieve relevant evidence that can answer the question from all knowledge sources:"
single_source_inst = "Given a question, retrieve relevant evidence that can answer the question from Text sources:"

sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]

# Prepend each sentence with the instruction
updated_sentences = [f"{single_source_inst} {sentence}" for sentence in sentences]

embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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## Training Details

### Framework Versions
- Python: 3.8.10
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- PyTorch: 2.0.0+cu118
- Accelerate: 0.34.0
- Datasets: 2.21.0
- Tokenizers: 0.19.1

### Sentence Transformers Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)


## Citation

If you find this resource useful in your research, please consider giving a like and citation.

```
@article{min2024unihgkr,
  title={UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers},
  author={Min, Dehai and Xu, Zhiyang and Qi, Guilin and Huang, Lifu and You, Chenyu},
  journal={arXiv preprint arXiv:2410.20163},
  year={2024}
}
```

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