Update README.md

README.md

@@ -2606,7 +2606,7 @@ license: mit
 
 # gte-base
 
-Gegeral Text Embeddings (GTE) model. 
+Gegeral Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281)
 
 The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.
 
@@ -2684,3 +2684,18 @@ print(cos_sim(embeddings[0], embeddings[1]))
 ### Limitation
 
 This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
+
+### Citation
+
+If you find our paper or models helpful, please consider citing them as follows:
+
+```
+@misc{li2023general,
+      title={Towards General Text Embeddings with Multi-stage Contrastive Learning}, 
+      author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
+      year={2023},
+      eprint={2308.03281},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```