File size: 3,373 Bytes
2852136
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
import json
import os
from typing import List

import datasets


_HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")
_DESCRIPTION = "Human preference data about helpfulness and harmlessness."
_CITATION = ""
_HOMEPAGE = "{}/datasets/Anthropic/hh-rlhf".format(_HF_ENDPOINT)
_LICENSE = "mit"
_URL = "{}/datasets/Anthropic/hh-rlhf/resolve/main/".format(_HF_ENDPOINT)
_URLS = {
    "train": [
        _URL + "harmless-base/train.jsonl.gz",
        _URL + "helpful-base/train.jsonl.gz",
        _URL + "helpful-online/train.jsonl.gz",
        _URL + "helpful-rejection-sampled/train.jsonl.gz",
    ],
    "test": [
        _URL + "harmless-base/test.jsonl.gz",
        _URL + "helpful-base/test.jsonl.gz",
        _URL + "helpful-online/test.jsonl.gz",
        _URL + "helpful-rejection-sampled/test.jsonl.gz",
    ],
}


class HhRlhfEn(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("0.0.0")

    def _info(self) -> datasets.DatasetInfo:
        features = datasets.Features(
            {
                "instruction": datasets.Value("string"),
                "chosen": datasets.Value("string"),
                "rejected": datasets.Value("string"),
                "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager):
        file_path = dl_manager.download_and_extract(_URLS)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": file_path["train"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": file_path["test"]}),
        ]

    def _generate_examples(self, filepaths: List[str]):
        key = 0
        for filepath in filepaths:
            with open(filepath, "r", encoding="utf-8") as f:
                for row in f:
                    data = json.loads(row)
                    chosen = data["chosen"]
                    rejected = data["rejected"]

                    assist_idx = rejected.rfind("\n\nAssistant: ")
                    r_reject = rejected[assist_idx + 13 :].strip()
                    assist_idx = chosen.rfind("\n\nAssistant: ")
                    r_accept = chosen[assist_idx + 13 :].strip()

                    human_idx = chosen.rfind("\n\nHuman: ")
                    query = chosen[human_idx + 9 : assist_idx].strip()
                    prompt = chosen[:human_idx]
                    history = []

                    while prompt.rfind("\n\nAssistant: ") != -1:
                        assist_idx = prompt.rfind("\n\nAssistant: ")
                        human_idx = prompt.rfind("\n\nHuman: ")
                        if human_idx != -1:
                            old_query = prompt[human_idx + 9 : assist_idx].strip()
                            old_resp = prompt[assist_idx + 13 :].strip()
                            history.insert(0, (old_query, old_resp))
                        else:
                            break
                        prompt = prompt[:human_idx]

                    yield key, {"instruction": query, "chosen": r_accept, "rejected": r_reject, "history": history}
                    key += 1