Datasets:
metadata
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 original 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, 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:
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()