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.
For the official code and further details, please refer to the GitHub repository.
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
- Code/GitHub Repository: https://github.com/Jasaxion/LexSemBridge/
- Documentation: Sentence Transformers Documentation
- Repository (Sentence Transformers Library): Sentence Transformers on GitHub
- Hugging Face (Sentence Transformers Models): Sentence Transformers on Hugging Face
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:
pip install -U sentence-transformers
Then you can load this model and run inference.
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
@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}
}