| language: | |
| - en | |
| license: apache-2.0 | |
| task_categories: | |
| - text-generation | |
| - fill-mask | |
| tags: | |
| - code | |
| - python | |
| - verl | |
| - repo-specific-finetuning | |
| pretty_name: Verl Code Corpus (File Holdout Split) | |
| size_categories: | |
| - n<1K | |
| # archit11/verl-code-corpus-track-a-file-split | |
| Repository-specific code corpus extracted from the `verl` project and split by file for training/evaluation. | |
| ## What is in this dataset | |
| - Source corpus: `data/code_corpus_verl` | |
| - Total files: 214 | |
| - Train files: 172 | |
| - Validation files: 21 | |
| - Test files: 21 | |
| - File type filter: .py | |
| - Split mode: `file` (file-level holdout) | |
| Each row has: | |
| - `file_name`: flattened source file name | |
| - `text`: full file contents | |
| ## Training context | |
| This dataset was used for extended pretraining of: | |
| - Model repo: `https://huggingface.co/archit11/qwen2.5-coder-3b-verl-track-a-lora` | |
| - Base model: `/root/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-3B/snapshots/09d9bc5d376b0cfa0100a0694ea7de7232525803` | |
| - Sequence curriculum: [768, 1024] | |
| - Learning rate: 0.0001 | |
| - Batch size: 8 | |
| Evaluation from this run: | |
| - Baseline perplexity (val/test): 3.1820 / 2.7764 | |
| - Post-training perplexity (val/test): 2.7844 / 2.2379 | |
| ## Load with datasets | |
| ```python | |
| from datasets import load_dataset | |
| ds = load_dataset("archit11/verl-code-corpus-track-a-file-split") | |
| print(ds) | |
| print(ds["train"][0]["file_name"]) | |
| ``` | |