Update README.md

README.md

@@ -12,6 +12,9 @@ language:
 tags:
 - qwen
 inference: false
+license: other
+license_name: tongyi-qianwen-license-agreement
+license_link: https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT
 ---
 
 # `rinna/nekomata-14b`
@@ -48,7 +51,7 @@ The name `nekomata` comes from the Japanese word [`猫又/ねこまた/Nekomata`
 
     `nekomata-14B` was trained on 16 nodes of Amazon EC2 trn1.32xlarge instance powered by AWS Trainium purpose-built ML accelerator chip. The pre-training job was completed within a timeframe of approximately 7 days.
 
-* **Authors**
+* **Contributors**
 
     - [Tianyu Zhao](https://huggingface.co/tianyuz)
     - [Akio Kaga](https://huggingface.co/rakaga)
@@ -118,10 +121,19 @@ We compared the `Qwen` tokenizer (as used in `nekomata`) and the `llama-2` token
 
 # How to cite
 ~~~
-@misc{RinnaNekomata14b, 
-    url={https://huggingface.co/rinna/nekomata-14b}, 
-    title={rinna/nekomata-14b}, 
+@misc{rinna-nekomata-14b,
+    title = {rinna/nekomata-14b},
     author={Zhao, Tianyu and Kaga, Akio and Sawada, Kei}
+    url = {https://huggingface.co/rinna/nekomata-14b},
+}
+
+@inproceedings{sawada2024release,
+    title = {Release of Pre-Trained Models for the {J}apanese Language},
+    author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
+    booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
+    month = {5},
+    year = {2024},
+    url = {https://arxiv.org/abs/2404.01657},
 }
 ~~~
 ---