Update README.md

README.md

@@ -86,7 +86,7 @@ We recorded 100,000 data samples along with their scores, creating the dataset `
 
 
 
-# abaltion experiments
+# Abaltion experiments
 After meticulously designed ablation studies, we aimed to contrast the effects between the Chinese-fineweb-edu dataset and traditional Chinese pre-training corpora. 
 For this purpose, we randomly selected samples from five datasets—CCI2-Data, SkyPile-150B, TeleChat-PTD, IndustryCorpus, and MAP-CC—proportional to the Chinese-fineweb-edu dataset, constructing a comparison dataset named chinese-random-select.
 In our experiments, we utilized a model with 2.1 billion parameters, training it for 65k steps on both datasets respectively.
@@ -97,6 +97,10 @@ The results distinctly show that the dataset trained on Chinese-fineweb-edu sign
 <img width="900px" alt="experiment" src="./chinese-fineweb-benchmark.png">
 </p>
 
+The experimental results reveal that in the later stages of training, as it enters the second epoch and the learning rate rapidly decreases, the model trained with the chinese-fineweb-edu data shows a significant increase in accuracy,
+whereas the model trained with randomly selected data remains at a lower level. This proves that the high-quality data of chinese-fineweb-edu significantly aids in training effectiveness.
+With the same training duration, it can enhance model capabilities faster and save training resources.
+This outcome also shares a striking similarity with the data ablation experiments conducted by HuggingFace on fineweb edu.
 
 **We warmly invite developers and researchers interested in this field to follow and engage with the community, working together to advance the technology. Stay tuned for the open-source release of the dataset!**
 
@@ -191,7 +195,8 @@ Chinese Fineweb Edu 数据集的原始数据来源广泛，涵盖了多个国内
 </p>
 
 
-通过实验结果可以发现，在训练的靠后阶段，由于进入了第2个epoch，且学习率进入快速下降阶段，此时，使用chinese-fineweb-edu训练的模型，准确率有了明显的上升，而使用随机抽取的数据训练，则一直处于较低水平。这证明了chinese-fineweb-edu高质量数据对于模型训练效果有显著帮助，在同样训练时间下，能够更快的提升模型能力，节省训练资源，这个结果也和HuggingFace fineweb edu 的数据消融实验有异曲同工之妙。
+通过实验结果可以发现，在训练的靠后阶段，由于进入了第2个epoch，且学习率进入快速下降阶段此时，使用chinese-fineweb-edu训练的模型，准确率有了明显的上升，而使用随机抽取的数据训练，则一直处于较低水平
+这证明了chinese-fineweb-edu高质量数据对于模型训练效果有显著帮助，在同样训练时间下，能够更快的提升模型能力，节省训练资源，这个结果也和HuggingFace fineweb edu 的数据消融实验有异曲同工之妙。