Update README.md

README.md

@@ -32,6 +32,7 @@ configs:
 - [🌐 Overview](#overview)
 - [📚 Preparation](#preparation)
 - [⏳ Data Selection](#data_selection)
+- [📈 Training](#training)
 - [📝 Citation](#citation)
 
 
@@ -40,7 +41,6 @@ configs:
 ## 🌐 Overview
 
 Long-context modeling has drawn more and more attention in the area of Large Language Models (LLMs). Continual training with long-context data becomes the de-facto method to equip LLMs with the ability to process long inputs. However, it still remains an open challenge to measure the quality of long-context training data. To address this issue, we propose a **L**ong-context data selection framework with **A**ttention-based **D**ependency **M**easurement (**LADM**), which can efficiently identify high-quality long-context data from a large-scale, multi-domain pre-training corpus. LADM leverages the retrieval capabilities of the attention mechanism to capture contextual dependencies, ensuring a comprehensive quality measurement of long-context data. Experimental results show that our LADM framework significantly boosts the performance of LLMs on multiple long-context tasks with only 1B tokens for continual training.
-![](./assets/framework.png)
 
 
 <a name="preparation"></a>
@@ -77,6 +77,13 @@ For full usage:
 bash launch.sh
 ```
 
+<a name="training"></a>
+
+## 📈 Training
+
+Our training mainly follows [Huggingface Trainer](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling) code base. Please refer to that repo for more details.
+
+
 <a name="citation"></a>
 
 ## 📝 Citation
@@ -89,4 +96,4 @@ If you find this repo useful for your research, please consider citing the paper
   journal={arXiv preprint arXiv:2503.02502},
   year={2025}
 }
-```
+```