mininlp / mininlp.py
from __future__ import absolute_import, division, print_function
import logging
import datasets
_CITATION = """
MiniNLP Data
"""
_DESCRIPTION = """
MiniNLP Data
"""
_URLS = {
"train": "train.tsv",
"dev": "dev.tsv",
"test": "test.tsv",
}
class MiniNLPConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
"""
Args:
**kwargs: keyword arguments forwarded to super.
"""
super().__init__(**kwargs)
class MiniNLP(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
MiniNLPConfig(
name="MiniNLP",
version=datasets.Version("1.0.0", ""),
description="MiniNLP Dataset For Models",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int32"),
"num": datasets.Value("int32"),
"query": datasets.Value("string"),
"doc": datasets.Value("string"),
"label": datasets.Value("string"),
"score": datasets.Value("float32"),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage="https://fuliucansheng.github.io/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": downloaded_files["train"]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": downloaded_files["dev"]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": downloaded_files["test"]},
),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logging.info("generating examples from = %s", filepath)
names = ["num", "query", "doc", "label", "score"]
with open(filepath, encoding="utf-8") as f:
for id_, line in enumerate(f):
values = line.strip("\n").split("\t")
row_dict = dict(zip(names, values))
yield id_, {
"id": id_,
"num": int(row_dict.get("num")),
"query": row_dict.get("query"),
"doc": row_dict.get("doc"),
"label": row_dict.get("label"),
"score": float(row_dict.get("score")),
}