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---
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- UniHGKR-base
widget: []
---
# UniHGKR-base
Our paper: [UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers](https://arxiv.org/abs/2410.20163).
Please see github repository [UniHGKR](https://github.com/ZhishanQ/UniHGKR/tree/main/code_for_UniHGKR_base) to know how to use this model.
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.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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()
)
```
## 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
## 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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