| --- |
| license: apache-2.0 |
| size_categories: |
| - 1M<n<10M |
| tags: |
| - code |
| dataset_info: |
| features: |
| - name: max_stars_count |
| dtype: int64 |
| - name: text |
| dtype: string |
| - name: token_count |
| dtype: int64 |
| splits: |
| - name: train |
| num_bytes: 10787104987 |
| num_examples: 2130812 |
| download_size: 3723229232 |
| dataset_size: 10787104987 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| |
| # Common Starcoder dataset |
|
|
| This dataset is generated from [bigcode/starcoderdata](https://huggingface.co/datasets/bigcode/starcoderdata). |
|
|
| Total GPT2 Tokens: 4,649,163,171 |
|
|
| ## Generation Process |
| 1. We filtered the original dataset with common language: C, Cpp, Java, Python and JSON. |
| 2. We removed some columns for mixing up with other dataset: "id", "max_stars_repo_path", "max_stars_repo_name" |
| 3. After removing the irrelevant fields, we shuffle the dataset with random seed=42. |
| 4. We filtered the data on "max_stars_count" > 300 and shuffle again. |
| 5. We further reduced the dataset size by select(range(current_size, 2_500_000)), However there are only 2.13M samples left. |
| 6. Add "n_tokens" by using GPT2Tokenizer to count the tokens in the "content" field. |
