Update README.md

README.md

@@ -8,7 +8,7 @@ High-quality Chinese text from Common Crawl cleaned by the following steps:
 - Documents whose language is identified as non-Chinese by fasttext are removed.
 - All text in Traditional Chinese is converted into Simplified Chinese.
 - Low-quality documents (e.g. boilerplates, advertisements) are heuristically removed based on statistics such as average line length, portion of special characters, etc.
-- Exact deduplication is performed in buckets of around 100GB compressed text. We did not deduplicate globally due to memory constraints, and estimate that about 0.03% of the documents are exact duplicates based on small-scale cross-bucket deduplication.
+- Exact deduplication.
 - Qwen2.5-32B-Instruct is used to generate language quality annotation (on a scale of 1-5) for 9.3M Chinese documents and 9.2M English documents, from which we sample 398K Chinese documents and 250K English documents to balance label distribution. An XLM-RoBERT-large classifier is trained with regression on these annotations. Any document receiving a score lower than 4 is removed.
 
 **Details about Model Annotations**
@@ -21,3 +21,7 @@ On 2K samples, we compared the annotation distribution (in percentage) of Qwen2.
 | 72B   | 0.3 | 4.7  | 22.9 | 58.1 | 14.1 |
 
 The scores between the two models have a correlation coefficient of 0.75, and manual inspection suggests that both are satisfactory. We eventually choose the 32B model for both efficiency and more balanced label distribution.
+
+**Data Statistics**
+- Number of samples: 623,807,180
+- Size: 1.1TB (2120 parquet files)