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---
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. |