Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
Chinese
Size:
10M - 100M
License:
Update README.md
Browse files
README.md
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@@ -84,6 +84,20 @@ We utilized OpenCSG's enterprise-grade large language model, csg-wukong-enterpri
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We recorded 100,000 data samples along with their scores, creating the dataset `fineweb_edu_classifier_chinese_data`. Using the scores from this dataset as labels, we trained a Chinese BERT model, `fineweb_edu_classifier_chinese`, which can assign a score of 0-5 to each input text. We plan to further optimize this scoring model, and in the future, the OpenCSG algorithm team will open-source the `fineweb_edu_classifier_chinese_data` and the `fineweb_edu_classifier_chinese scoring model` to further promote community development and collaboration. This dataset contains meticulously annotated and scored educational text data, providing high-quality training data for researchers and developers.
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**We warmly invite developers and researchers interested in this field to follow and engage with the community, working together to advance the technology. Stay tuned for the open-source release of the dataset!**
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## License Agreement
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We recorded 100,000 data samples along with their scores, creating the dataset `fineweb_edu_classifier_chinese_data`. Using the scores from this dataset as labels, we trained a Chinese BERT model, `fineweb_edu_classifier_chinese`, which can assign a score of 0-5 to each input text. We plan to further optimize this scoring model, and in the future, the OpenCSG algorithm team will open-source the `fineweb_edu_classifier_chinese_data` and the `fineweb_edu_classifier_chinese scoring model` to further promote community development and collaboration. This dataset contains meticulously annotated and scored educational text data, providing high-quality training data for researchers and developers.
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# abaltion experiments
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After meticulously designed ablation studies, we aimed to contrast the effects between the Chinese-fineweb-edu dataset and traditional Chinese pre-training corpora.
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For this purpose, we randomly selected samples from five datasets—CCI2-Data, SkyPile-150B, TeleChat-PTD, IndustryCorpus, and MAP-CC—proportional to the Chinese-fineweb-edu dataset, constructing a comparison dataset named chinese-random-select.
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In our experiments, we utilized a model with 2.1 billion parameters, training it for 65k steps on both datasets respectively.
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Throughout the training, we periodically saved checkpoints of the model and conducted validations on Chinese evaluation benchmarks CEval and CMMLU.
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The graph below displays the performance trends of these two datasets in evaluation tasks.
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The results distinctly show that the dataset trained on Chinese-fineweb-edu significantly outperforms the chinese-random-select dataset in both evaluation tasks, especially demonstrating considerable advantages in the later stages of training. This underscores the effectiveness and adaptability of Chinese-fineweb-edu in Chinese language tasks. Furthermore, these experimental outcomes also highlight the critical impact of dataset selection and construction on the ultimate performance of models.
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<p align="center">
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<img width="900px" alt="experiment" src="./chinese-fineweb-benchmark.png">
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</p>
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**We warmly invite developers and researchers interested in this field to follow and engage with the community, working together to advance the technology. Stay tuned for the open-source release of the dataset!**
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## License Agreement
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