| --- |
| license: openrail |
| dataset_info: |
| features: |
| - name: hexsha |
| dtype: string |
| - name: size |
| dtype: int64 |
| - name: content |
| dtype: string |
| - name: avg_line_length |
| dtype: float64 |
| - name: max_line_length |
| dtype: int64 |
| - name: alphanum_fraction |
| dtype: float64 |
| splits: |
| - name: train |
| num_bytes: 3582248477.9086223 |
| num_examples: 806789 |
| - name: test |
| num_bytes: 394048264.9973618 |
| num_examples: 88747 |
| - name: valid |
| num_bytes: 3982797.09401595 |
| num_examples: 897 |
| download_size: 1323156008 |
| dataset_size: 3980279540 |
| task_categories: |
| - text-generation |
| language: |
| - code |
| tags: |
| - code |
| pretty_name: TheStack-Java |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| ## Dataset 1: TheStack - Java - Cleaned |
|
|
| **Description**: This dataset is drawn from TheStack Corpus, an open-source code dataset with over 3TB of GitHub data covering 48 programming languages. We selected a small portion of this dataset to optimize smaller language models for Java, a popular statically typed language. |
|
|
| **Target Language**: Java |
|
|
| **Dataset Size**: |
| - Training: 900,000 files |
| - Validation: 50,000 files |
| - Test: 50,000 files |
|
|
| **Preprocessing**: |
| 1. Selected Java as the target language due to its popularity on GitHub. |
| 2. Filtered out files with average line length > 100 characters, maximum line length > 1000 characters, and alphabet ratio < 25%. |
| 3. Split files into 90% training, 5% validation, and 5% test sets. |
|
|
| **Tokenizer**: Byte Pair Encoding (BPE) tokenizer with tab and whitespace tokens. GPT-2 vocabulary extended with special tokens. |
|
|
| **Training Sequences**: Sequences constructed by joining training data text to reach a context length of 2048 tokens (1024 tokens for full fine-tuning). |
