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README.md

@@ -1,19 +1,24 @@
 ---
-{}
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+license: mit
 ---
-# LLARA-7B-Passage
 
-This model is fine-tuned from LLaMA-2-7B using LoRA and the embedding size is 4096.
+For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding
 
-## Training Data
+# LLARA ([paper](https://arxiv.org/pdf/2312.15503))
 
-The model is fine-tuned on the training split of [MS MARCO Passage Ranking](https://microsoft.github.io/msmarco/Datasets) datasets for 1 epoch. Please check our paper for details.
+In this project, we introduce LLaRA:
+- EBAE: Embedding-Based Auto-Encoding.
+- EBAR: Embedding-Based Auto-Regression. 
 
-## Usage
 
-Below is an example to encode a query and a passage, and then compute their similarity using their embedding.
+## Usage
 
-```python
+```
 import torch
 from transformers import AutoModel, AutoTokenizer, LlamaModel
 
@@ -92,6 +97,27 @@ with torch.no_grad():
     score = query_embedding @ passage_embeddings.T
     print(score)
 
+```
+
+
+## Acknowledgement
+
+Thanks to the authors of open-sourced datasets, including MSMARCO, BEIR, etc. 
+Thanks to the open-sourced libraries like [Pyserini](https://github.com/castorini/pyserini).
+
+
+
+## Citation
 
+If you find this repository useful, please consider giving a star :star: and citation
 
+```
+@misc{li2023making,
+      title={Making Large Language Models A Better Foundation For Dense Retrieval}, 
+      author={Chaofan Li and Zheng Liu and Shitao Xiao and Yingxia Shao},
+      year={2023},
+      eprint={2312.15503},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
 ```