scripts/halueval-upload-cli.py

#!/usr/bin/env python3

import random
import requests

from datasets import load_dataset, Dataset, DatasetDict


path = 'pminervini/HaluEval'

API_URL = f"https://datasets-server.huggingface.co/splits?dataset={path}"
response = requests.get(API_URL)
res_json = response.json()

gold_splits = {'dialogue', 'qa', 'summarization', 'general'}

available_splits = {split['config'] for split in res_json['splits']} if 'splits' in res_json else set()

name_to_ds = dict()

for name in gold_splits:
    ds = load_dataset("json", data_files={'data': f"data/{name}_data.json"})
    name_to_ds[name] = ds
    # if name not in available_splits:
    ds.push_to_hub(path, config_name=name)

def list_to_dict(lst: list) -> dict:
    res = dict()
    for entry in lst:
        for k, v in entry.items():
            if k not in res:
                res[k] = []
            res[k] += [v]
    return res

for name in (gold_splits - {'general'}):
    random.seed(42)
    ds = name_to_ds[name]
    new_entry_lst = []
 
    for entry in ds['data']:
        is_hallucinated = random.random() > 0.5
        if name in {'qa'}:
            new_entry = {
                'knowledge': entry['knowledge'],
                'question': entry['question'],
                'answer': entry[f'{"hallucinated" if is_hallucinated else "right"}_answer'],
                'hallucination': 'yes' if is_hallucinated else 'no'
            }
            new_entry_lst += [new_entry]
        if name in {'dialogue'}:
            new_entry = {
                'knowledge': entry['knowledge'],
                'dialogue_history': entry['dialogue_history'],
                'response': entry[f'{"hallucinated" if is_hallucinated else "right"}_response'],
                'hallucination': 'yes' if is_hallucinated else 'no'
            }
        if name in {'summarization'}:
            new_entry = {
                'document': entry['document'],
                'summary': entry[f'{"hallucinated" if is_hallucinated else "right"}_summary'],
                'hallucination': 'yes' if is_hallucinated else 'no'
            }

    new_ds_map = list_to_dict(new_entry_lst)
    new_ds = Dataset.from_dict(new_ds_map)
    new_dsd = DatasetDict({'data': new_ds})

    new_dsd.push_to_hub(path, config_name=f'{name}_samples')