CCI3.0-HQ: a large-scale Chinese dataset of high quality designed for pre-training large language models
This work addresses the need for high-quality Chinese data for language model pre-training, though it is incremental as it builds on existing datasets with improved filtering.
The researchers tackled the problem of low-quality data for pre-training large language models in Chinese by creating CCI3.0-HQ, a 500GB high-quality dataset, and demonstrated its effectiveness by training a 0.5B parameter model that achieved superior performance on 10 benchmarks in zero-shot settings compared to existing datasets.
We present CCI3.0-HQ (https://huggingface.co/datasets/BAAI/CCI3-HQ), a high-quality 500GB subset of the Chinese Corpora Internet 3.0 (CCI3.0)(https://huggingface.co/datasets/BAAI/CCI3-Data), developed using a novel two-stage hybrid filtering pipeline that significantly enhances data quality. To evaluate its effectiveness, we trained a 0.5B parameter model from scratch on 100B tokens across various datasets, achieving superior performance on 10 benchmarks in a zero-shot setting compared to CCI3.0, SkyPile, and WanjuanV1. The high-quality filtering process effectively distills the capabilities of the Qwen2-72B-instruct model into a compact 0.5B model, attaining optimal F1 scores for Chinese web data classification. We believe this open-access dataset will facilitate broader access to high-quality language models.