---
datasets:
- Jasaxion/LexSemBridge_eval
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- lexsembridge
widget: []
---

# LexSemBridge: Fine-Grained Dense Representation Enhancement through Token-Aware Embedding Augmentation

This model implements **LexSemBridge**, a unified framework that enhances dense query representations through fine-grained, input-aware vector modulation. LexSemBridge constructs latent enhancement vectors from input tokens using statistical, learned, and contextual paradigms, integrating them with dense embeddings via element-wise interaction. It operates as a plug-in without modifying the backbone encoder and naturally extends to both text and vision modalities, aiming to improve performance on fine-grained retrieval tasks where precise keyword alignment and span-level localization are crucial.

The model is based on the paper [LexSemBridge: Fine-Grained Dense Representation Enhancement through Token-Aware Embedding Augmentation](https://huggingface.co/papers/2508.17858).

For the official code and further details, please refer to the [GitHub repository](https://github.com/Jasaxion/LexSemBridge/).

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity

### Model Sources

- **Paper:** [LexSemBridge: Fine-Grained Dense Representation Enhancement through Token-Aware Embedding Augmentation](https://huggingface.co/papers/2508.17858)
- **Code/GitHub Repository:** https://github.com/Jasaxion/LexSemBridge/
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository (Sentence Transformers Library):** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face (Sentence Transformers Models):** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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

### 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("Jasaxion/LexSemBridge_CLR_snowflake") # Example: LexSemBridge-CLR-snowflake model
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

## Training Details

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.0.dev0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 2.21.0
- Tokenizers: 0.19.1

## Citation

### BibTeX
```bibtex
@article{wu2025lexsembridge,
        title={LexSemBridge: Fine-Grained Dense Representation Enhancement through Token-Aware Embedding Augmentation},
        author={Wu, Zhiyong and Wu, Zhenyu and Xu, Fangzhi and Wang, Yian and Sun, Qiushi and Jia, Chengyou and Cheng, Kanzhi and Ding, Zichen and Chen, Liheng and Liang, Paul Pu and others},
        journal={arXiv preprint arXiv:2508.17858},
        year={2025}
      }
```