Datasets:
Download scripts/load_dataset.py from Prajjit/CodeMixCheck: direct link, hf CLI and curl.
- Browser
- Download file 2.57 kB
-
https://huggingface.co/datasets/Prajjit/CodeMixCheck/resolve/main/scripts/load_dataset.py
- Command line
-
hf download hf://datasets/Prajjit/CodeMixCheck/scripts/load_dataset.py
-
curl -L -o load_dataset.py https://huggingface.co/datasets/Prajjit/CodeMixCheck/resolve/main/scripts/load_dataset.py
2.57 kB
| #!/usr/bin/env python3 | |
| """Loading examples for CodeMixCheck with the Hugging Face `datasets` library. | |
| python scripts/load_dataset.py # load from this local folder | |
| python scripts/load_dataset.py --repo <user>/CodeMixCheck # load from the Hub (after upload) | |
| For a private or gated Hub repo, log in first (`huggingface-cli login`) and accept | |
| the access conditions on the dataset page. | |
| """ | |
| from __future__ import annotations | |
| import argparse | |
| from collections import Counter | |
| from pathlib import Path | |
| from datasets import load_dataset | |
| LOCAL = str(Path(__file__).resolve().parents[1]) | |
| def main(): | |
| ap = argparse.ArgumentParser() | |
| ap.add_argument("--repo", default=LOCAL, help="Hub repo id or local path (default: this folder)") | |
| repo = ap.parse_args().repo | |
| # 1. Load each configuration (all splits) | |
| fv = load_dataset(repo, "fact_verification") | |
| sent = load_dataset(repo, "sentiment") | |
| abuse = load_dataset(repo, "abuse_detection") | |
| for name, ds in [("fact_verification", fv), ("sentiment", sent), ("abuse_detection", abuse)]: | |
| print(name, {k: v.num_rows for k, v in ds.items()}) | |
| # 2. Load an individual split | |
| train = load_dataset(repo, "fact_verification", split="train") | |
| print("fact_verification/train:", train.num_rows, "rows") | |
| # 3. Access records | |
| rec = train[0] | |
| print({k: rec[k] for k in ("id", "text", "label", "label_3way", "evidence_explanation", "provenance_class")}) | |
| # 4. Inspect labels | |
| print("train labels:", Counter(train["label"])) | |
| print("label origins:", Counter(train["label_origin"])) | |
| # 5. Synthetic test vs curated test (never merge them when reporting results) | |
| test_syn = load_dataset(repo, "fact_verification", split="test_synthetic") | |
| print("test_synthetic:", test_syn.num_rows) | |
| if "test_curated" in fv: | |
| cur = fv["test_curated"] | |
| for subset in sorted(set(cur["eval_subset"])): | |
| part = cur.filter(lambda r, s=subset: r["eval_subset"] == s) | |
| print("test_curated /", subset, part.num_rows, Counter(part["label"])) | |
| else: | |
| print("test_curated is not available in this copy.") | |
| # 6. Optional: drop rows flagged for cross-split overlap to get a stricter evaluation | |
| strict = test_syn.filter(lambda r: not ({"D01", "D02"} & set(r["audit_flags"]))) | |
| print("test_synthetic without cross-split near-duplicate / same-claim rows:", strict.num_rows) | |
| # 7. Reproduce the statistics in README.md | |
| # python scripts/dataset_statistics.py | |
| if __name__ == "__main__": | |
| main() | |