# 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.
region:us
{"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-*"}]}]}
TAGS
#region-us 

# Dataset Card for "starcoder_labeled"

Starcoder data, 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.
# Dataset Card for "starcoder_labeled"

Starcoder data, 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.
TAGS
#region-us 
 # Dataset Card for "starcoder_labeled"

Starcoder data, 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.
passage: TAGS
#region-us 
# Dataset Card for "starcoder_labeled"

Starcoder data, 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.