Upload ko-lima.py

ko-lima.py

@@ -0,0 +1,68 @@
+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("lima_train_deepl_ko.jsonl")}),
+ datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath":dl_manager.download("lima_test_deepl_ko.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