Improve model card for LexSemBridge: Add paper/code links, update pipeline tag, license, and datasets

README.md

@@ -1,36 +1,42 @@
 ---
-datasets: []
-language: []
+datasets:
+- Jasaxion/LexSemBridge_eval
+language:
+- en
 library_name: sentence-transformers
-pipeline_tag: sentence-similarity
+license: apache-2.0
+pipeline_tag: feature-extraction
 tags:
 - sentence-transformers
 - sentence-similarity
 - feature-extraction
+- lexsembridge
 widget: []
 ---
 
-# SentenceTransformer
+# LexSemBridge: Fine-Grained Dense Representation Enhancement through Token-Aware Embedding Augmentation
 
-This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+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
-<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
 - **Maximum Sequence Length:** 512 tokens
 - **Output Dimensionality:** 1024 tokens
 - **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
 
 ### 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 on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+- **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
 
@@ -57,7 +63,7 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
+model = SentenceTransformer("Jasaxion/LexSemBridge_CLR_snowflake") # Example: LexSemBridge-CLR-snowflake model
 # Run inference
 sentences = [
     'The weather is lovely today.',
@@ -74,42 +80,6 @@ print(similarities.shape)
 # [3, 3]
 ```
 
-<!--
-### Direct Usage (Transformers)
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-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
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-
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-</details>
--->
-
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-### Out-of-Scope Use
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-## Bias, Risks and Limitations
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-### Recommendations
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-
 ## Training Details
 
 ### Framework Versions
@@ -124,21 +94,11 @@ You can finetune this model on your own dataset.
 ## Citation
 
 ### BibTeX
-
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
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-<!--
-## Model Card Authors
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+```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}
+      }
+```