Update README.md

README.md

@@ -1,3 +1,18 @@
 ---
 license: mit
+task_categories:
+- text-generation
 ---
+
+We collect a 2.5B training dataset from various domains for long-context continual pre-training. The composition of this dataset is as follows (partially inspired by [Long-Data-Collection](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections)):
+
+| Domain | Proportion | Source |
+| ------------- | ---------- | ------ |
+| Book | 40% | [Redpajama-Book](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) |
+| Arxiv | 20% | [Redpajama-Arxiv](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) |
+| General | 20% | [Redpajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) |
+| Code | 10% | [LCC-Python](https://huggingface.co/datasets/microsoft/LCC_python) |
+| QA | 5% | [Natural Questions](https://ai.google.com/research/NaturalQuestions/) |
+| Summarization | 5% | [BookSum](https://github.com/salesforce/booksum) |
+
+We have also curated a test dataset comprising 250 million tokens, mirroring the same composition. The selection criteria ensured that the average n-gram similarity (for n=2, 3, 4) with the training set is below 10%. This threshold effectively excludes all QA and Summarization data, resulting in a test corpus where the distribution of tokens across Book, Arxiv, General, and Code categories follows a ratio of 4:2:2:1, respectively.