--- dataset_info: - config_name: algebraic-stack features: - name: text dtype: string - name: meta struct: - name: alphanum_fraction dtype: float64 - name: author dtype: string - name: avg_line_length dtype: float64 - name: converted dtype: bool - name: ext dtype: string - name: file dtype: string - name: hexsha dtype: string - name: include dtype: bool - name: lang dtype: string - name: length dtype: int64 - name: llama_tokens dtype: int64 - name: mathlib_filename dtype: string - name: max_forks_count dtype: float64 - name: max_forks_repo_forks_event_max_datetime dtype: string - name: max_forks_repo_forks_event_min_datetime dtype: string - name: max_forks_repo_head_hexsha dtype: string - name: max_forks_repo_licenses sequence: string - name: max_forks_repo_name dtype: string - name: max_forks_repo_path dtype: string - name: max_issues_count dtype: float64 - name: max_issues_repo_head_hexsha dtype: string - name: max_issues_repo_issues_event_max_datetime dtype: string - name: max_issues_repo_issues_event_min_datetime dtype: string - name: max_issues_repo_licenses sequence: string - name: max_issues_repo_name dtype: string - name: max_issues_repo_path dtype: string - name: max_line_length dtype: int64 - name: max_stars_count dtype: float64 - name: max_stars_repo_head_hexsha dtype: string - name: max_stars_repo_licenses sequence: string - name: max_stars_repo_name dtype: string - name: max_stars_repo_path dtype: string - name: max_stars_repo_stars_event_max_datetime dtype: string - name: max_stars_repo_stars_event_min_datetime dtype: string - name: num_tokens dtype: int64 - name: path dtype: string - name: reason dtype: string - name: repo dtype: string - name: save_path dtype: string - name: sha dtype: string - name: size dtype: int64 splits: - name: train num_bytes: 31797979222 num_examples: 3404654 - name: validation num_bytes: 165884973 num_examples: 18040 - name: test num_bytes: 162752298 num_examples: 18000 download_size: 11905060512 dataset_size: 32126616493 - config_name: arxiv features: - name: text dtype: string - name: meta struct: - name: arxiv_id dtype: string - name: language dtype: string - name: timestamp dtype: string - name: url dtype: string - name: yymm dtype: string splits: - name: train num_bytes: 88423197439 num_examples: 1542673 - name: validation num_bytes: 463620511 num_examples: 7793 - name: test num_bytes: 473629411 num_examples: 7840 download_size: 40591755551 dataset_size: 89360447361 - config_name: open-web-math features: - name: url dtype: string - name: text dtype: string - name: date dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 56086326272 num_examples: 6252080 - name: validation num_bytes: 281648006 num_examples: 31576 - name: test num_bytes: 284020779 num_examples: 31577 download_size: 27232284726 dataset_size: 56651995057 configs: - config_name: algebraic-stack data_files: - split: train path: algebraic-stack/train-* - split: validation path: algebraic-stack/validation-* - split: test path: algebraic-stack/test-* - config_name: arxiv data_files: - split: train path: arxiv/train-* - split: validation path: arxiv/validation-* - split: test path: arxiv/test-* - config_name: open-web-math data_files: - split: train path: open-web-math/train-* - split: validation path: open-web-math/validation-* - split: test path: open-web-math/test-* task_categories: - text-generation --- ![The header image of EleutherAI/proof-pile-2 edited to say "fixed"](./proofpile_fixed.png) The original [EleutherAI/proof-pile-2](https://huggingface.co/datasets/EleutherAI/proof-pile-2) dataset uses a custom python script and .jsonl.zst files, which some versions of the datasets library struggle with. This dataset contains the same data, subsets, and splits as [EleutherAI/proof-pile-2](https://huggingface.co/datasets/EleutherAI/proof-pile-2), converted into standard parquet format. Each subset and split was also shuffled so that you can directly train on the data without issue. Conversion was performed using the following script: ```python import os import zstandard as zstd import json import pandas as pd from tqdm import tqdm import datasets import huggingface_hub as hf DATA_URL = "EleutherAI/proof-pile-2" SUBSETS = [ "algebraic-stack", "arxiv", "open-web-math" ] SPLITS = [ "train", "validation", "test" ] LOCAL_DIR = "./local_data/proof-pile-2" OUT_URL = 'aklein4/proof-pile-2-fixed' def download_data( url: str, subset: str, split: str, ): hf.snapshot_download( repo_id=url, repo_type="dataset", allow_patterns=[f"{subset}/{split}/*"], local_dir=LOCAL_DIR, ) return os.path.join(LOCAL_DIR, subset, split) def format_data( url: str, subset: str , split: str, ): # download the data folder = download_data(url, subset, split) # get all files in the local dir data_files = [ os.path.join(folder, f) for f in os.listdir(folder) if f.endswith(".zst") ] # read all of the .jsonl.zst files examples = [] for file_path in tqdm(data_files): with zstd.open(open(file_path, "rb"), "rt", encoding="utf-8") as f: for x in f.readlines(): examples.append(json.loads(x)) # get the dataset df = pd.DataFrame(examples) dataset = datasets.Dataset.from_pandas(df) dataset = dataset.shuffle(seed=42) dataset.push_to_hub( OUT_URL, config_name=subset, split=split, private=False ) def main(): for subset in SUBSETS: for split in SPLITS: format_data( DATA_URL, subset, split, ) if __name__ == "__main__": main() ```