Update README.md

README.md

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-This is my reproduction of the Microsoft team's work, WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models. It is fully based on open-source models to construct training data and adopt supervised fine-tuning (SFT) to train the model. The results on code generation benchmarks like Humaneval (Humaneval+) and MBPP (MBPP+) are as follows: 79.9 (75.4), 75.8 (64.5). These results are excellent, confirming that the idea of 'learning from expert battles' proposed in the paper has great potential. I have also published the training data constructed during my reproduction of the paper in another repository, and everyone is welcome to use it.
-Original paper link: https://arxiv.org/pdf/2412.17395
- I have also published the training data constructed during my reproduction of the paper in another repository: https://huggingface.co/datasets/HuggingMicah/warrior_reproduce .
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-Also, I reproduced the experimental results in the paper. There are some differences from the original results, and I have marked the distinctions with underlines.
+This is my reproduction of the Microsoft team's work, WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models. It is fully based on open-source models to construct training data and adopt supervised fine-tuning (SFT) to train the model. Also, I reproduced the experimental results in the paper. These results are excellent, confirming that the idea of 'learning from expert battles' proposed in the paper has great potential. I have also published the training data constructed during my reproduction of the paper in another repository, and everyone is welcome to use it. Original paper link: https://arxiv.org/pdf/2412.17395  I have also published the training data constructed during my reproduction of the paper in another repository: https://huggingface.co/datasets/HuggingMicah/warrior_reproduce .
  | Models | Matplotlib (155) | NumPy (220) | Pandas (291) | PyTorch (68) | SciPy (106) | Sklearn (115) | TensorFlow (45) | Overall (1000) |
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |
 | INCODER (6.7B) | 28.3 | 4.4 | 3.1 | 4.4 | 2.8 | 2.8 | 3.8 | 7.4 |
@@ -23,4 +19,4 @@ Also, I reproduced the experimental results in the paper. There are some differe
 | WizardCoder-SC (15B) | 51.4 | 45.3 | 61.6 | 50.7 |
 | Magicoder-CL (6.7B) | 60.4 | 55.7 | 64.2 | 52.5 |
 | MagicoderS-CL (6.7B) | 70.7 | 66.4 | 68.3 | 56.4 |
-| WarriorCoder (6.7B) | 80.5 | 75.6 | 76.2 | 64.8 |
+| WarriorCoder (6.7B) | 79.9 | 75.4 | 75.8 | 64.5 |