---
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 naturally extends to both text and vision modalities with an appropriate tokenization, 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{zhan2025lexsembridge,
  title={LexSemBridge: Fine-Grained Dense Representation Enhancement through Token-Aware Embedding Augmentation},
  author={Zhan, Shaoxiong and Lin, Hai and Tan, Hongming and Cai, Xiaodong and Zheng, Hai-Tao and Su, Xin and Shan, Zifei and Liu, Ruitong and Kim, Hong-Gee},
  journal={arXiv preprint arXiv:2508.17858},
  year={2025}
}
```