lima / lima.py
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import json
import datasets
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
A high-quality dataset for efficient instruction tuning.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "other"
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
}
class LimaConfig(datasets.BuilderConfig):
"""BuilderConfig"""
def __init__(self, **kwargs):
"""BuilderConfig
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(LimaConfig, self).__init__(**kwargs)
class Lima(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
LimaConfig(
name="plain_text",
version=datasets.Version("0.0.1", ""),
description="Plain text",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"conversations": datasets.features.Sequence(datasets.Value("string")),
"source": datasets.Value("string"),
}
),
)
def _split_generators(self, dl_manager):
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_manager.download("train.jsonl")}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath":dl_manager.download("test.jsonl")})
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
key = 0
with open(filepath) as f:
for line in f.readlines():
instance = json.loads(line)
yield key, instance
key += 1