| | --- |
| | dataset_info: |
| | features: |
| | - name: code |
| | dtype: string |
| | - name: repo_path |
| | dtype: string |
| | - name: parsed_code |
| | dtype: string |
| | - name: quality_prob |
| | dtype: float64 |
| | - name: learning_prob |
| | dtype: float64 |
| | splits: |
| | - name: train |
| | num_bytes: 852705076967 |
| | num_examples: 65509810 |
| | download_size: 0 |
| | dataset_size: 852705076967 |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - split: train |
| | path: data/train-* |
| | --- |
| | # Dataset Card for "starcoder_labeled" |
| | |
| | [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. |
| | |
| | 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. |
