Update README.md

README.md

@@ -59,16 +59,16 @@ This model achieves an eval_loss of 0.419 at step 7,600 (approximately 10.90 epo
 - Collate more Go data, particularly self play data. It is quite clear that the size of the existing data is quite trivial compared to modern language model.
 
 # Reference
-[1] Silver, D., Huang, A., Maddison, C. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).
-[2] D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, et al., “Mastering chess and shogi by self-play with a general reinforcement learning algorithm,” arXiv preprint arXiv:1712.01815, 2017.
-[3] Coulom, R. Efficient selectivity and backup operators in Monte-Carlo tree search. In 5th International Conference on Computer and Games, 72–83 (2006).
-[4] Kocsis, L. & Szepesvari, C. Bandit based Monte-Carlo planning. In ´ 15th European Conference on Machine Learning, 282–293 (2006).
-[5] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000–6010, 2017.
-[6] Radford, Alec and Karthik Narasimhan. “Improving Language Understanding by Generative Pre-Training.” (2018).
-[7] D. Noever, M. Ciolino, and J. Kalin. The Chess Transformer: Mastering Play using Generative Language Models, Sept. 2020.
-[8] Zhang, Edwin et al. “Transcendence: Generative Models Can Outperform The Experts That Train Them.” (2024).
-[9] Ciolino, Matthew et al. “The Go Transformer: Natural Language Modeling for Game Play.” 2020 Third International Conference on Artificial Intelligence for Industries (AI4I) (2020): 23-26.
-[10] Radford, Alec et al. “Language Models are Unsupervised Multitask Learners.” (2019).
+[1] Silver, D., Huang, A., Maddison, C. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).  
+[2] D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, et al., “Mastering chess and shogi by self-play with a general reinforcement learning algorithm,” arXiv preprint arXiv:1712.01815, 2017.  
+[3] Coulom, R. Efficient selectivity and backup operators in Monte-Carlo tree search. In 5th International Conference on Computer and Games, 72–83 (2006).  
+[4] Kocsis, L. & Szepesvari, C. Bandit based Monte-Carlo planning. In ´ 15th European Conference on Machine Learning, 282–293 (2006).  
+[5] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000–6010, 2017.  
+[6] Radford, Alec and Karthik Narasimhan. “Improving Language Understanding by Generative Pre-Training.” (2018).  
+[7] D. Noever, M. Ciolino, and J. Kalin. The Chess Transformer: Mastering Play using Generative Language Models, Sept. 2020.  
+[8] Zhang, Edwin et al. “Transcendence: Generative Models Can Outperform The Experts That Train Them.” (2024).  
+[9] Ciolino, Matthew et al. “The Go Transformer: Natural Language Modeling for Game Play.” 2020 Third International Conference on Artificial Intelligence for Industries (AI4I) (2020): 23-26.  
+[10] Radford, Alec et al. “Language Models are Unsupervised Multitask Learners.” (2019).  
 
 ## Citation