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--- |
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dataset_info: |
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features: |
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- name: code |
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dtype: string |
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- name: repo_path |
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dtype: string |
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- name: parsed_code |
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dtype: string |
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- name: quality_prob |
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dtype: float64 |
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- name: learning_prob |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 852705076967 |
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|
num_examples: 65509810 |
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download_size: 0 |
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dataset_size: 852705076967 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# Dataset Card for "starcoder_labeled" |
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[Starcoder data](https://huggingface.co/datasets/bigcode/starcoderdata), with several popular languages selected, short sequences filtered out, then labeled based on learning quality (educational value) and code quality. |
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A good heuristic is to take anything with `>.5` code quality and `>.3` learning quality. But you may want to vary the thresholds by language, depending on your target task. |
