update readme

README.md

@@ -70,18 +70,18 @@ We utilize the MiniCPM-1.2B model architecture with the MiniCPM3-4B tokenizer. E
 Detailed evaluation results are reported below:
 
 - **Individual data experiments.** We perform isolated training runs using single datasets, facilitating direct comparisons between differently processed data from identical sources.
-<img src="assets/individual-english-table.png" alt="Performance Scatter Plot" width="75%">
-<img src="assets/individual-chinese-table.png" alt="Performance Scatter Plot" width="75%">
-<img src="assets/individual-plot.png" alt="Performance Scatter Plot" width="100%">
+<img src="assets/individual-english-table.png" alt="Individual English Table" width="75%">
+<img src="assets/individual-chinese-table.png" alt="Individual Chinese Table" width="75%">
+<img src="assets/individual-plot.png" alt="Individual Plot" width="100%">
 
 - **Mixed Data Experiments.** We use a mix of 60% English data, 30% Chinese data, and 10% code data (StarCoder-v2).
-<img src="assets/mix-table.png" alt="Performance Scatter Plot" width="75%">
-<img src="assets/mix-plot.png" alt="Performance Scatter Plot" width="100%">
+<img src="assets/mix-table.png" alt="Mix Table" width="75%">
+<img src="assets/mix-plot.png" alt="Mix Plot" width="100%">
 
 - **Loss and Performance Estimation Results.** We use the performance estimation methods proposed in [Densing Law](https://arxiv.org/abs/2412.04315) for further analysis and verification of the effectiveness of Ultra-FineWeb.
 
-<img src="assets/densing-law-table.png" alt="Performance Scatter Plot" width="75%">
-<img src="assets/densing-law-plot.png" alt="Performance Scatter Plot" width="100%">
+<img src="assets/densing-law-table.png" alt="Densing Law Table" width="75%">
+<img src="assets/densing-law-plot.png" alt="Densing Law Plot" width="100%">
 
 ## ❤️ Acknowledgements