Update README.md

README.md

@@ -2616,7 +2616,7 @@ The models are built upon the `transformer++` encoder [backbone](https://hugging
 The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)).
 
 We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct),
-a SOTA instruction-tuned bilingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.
+a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.
 
 <!-- Provide a longer summary of what this model is. -->
 
@@ -2630,7 +2630,7 @@ a SOTA instruction-tuned bilingual embedding model that ranked 2nd in MTEB and 1
 
 | Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo |
 |:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: |
-|[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| Chinese, English | 7720 | 32768 | 4096 | 67.34 | 87.57 |
+|[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| multi lingual | 7720 | 32768 | 4096 | 67.34 | 87.57 |
 |[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 434 | 8192 | 1024 | 65.39 | 86.71 |
 |[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 |
 
@@ -2691,8 +2691,8 @@ print(cos_sim(embeddings[0], embeddings[1]))
 ### Training Data
 
 - Masked language modeling (MLM): `c4-en`
-- Weak-supervised contrastive (WSC) pre-training: GTE pre-training data
-- Supervised contrastive fine-tuning: GTE fine-tuning data
+- Weak-supervised contrastive (WSC) pre-training: [GTE](https://arxiv.org/pdf/2308.03281.pdf) pre-training data
+- Supervised contrastive fine-tuning: GTE(https://arxiv.org/pdf/2308.03281.pdf) fine-tuning data
 
 ### Training Procedure 
 
@@ -2737,14 +2737,15 @@ The gte evaluation setting: `mteb==1.2.0, fp16 auto mix precision, max_length=81
 
 
 
-## Citation [TODO]
+## Citation
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+If you find our paper or models helpful, please consider citing them as follows:
 
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
+```
+@article{li2023towards,
+  title={Towards general text embeddings with multi-stage contrastive learning},
+  author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
+  journal={arXiv preprint arXiv:2308.03281},
+  year={2023}
+}
+```