File size: 4,177 Bytes
207ed8c |
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 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
"""TODO(art): Add a description here."""
from __future__ import absolute_import, division, print_function
import json
import os
import datasets
# TODO(art): BibTeX citation
_CITATION = """\
@InProceedings{anli,
author = {Chandra, Bhagavatula and Ronan, Le Bras and Chaitanya, Malaviya and Keisuke, Sakaguchi and Ari, Holtzman
and Hannah, Rashkin and Doug, Downey and Scott, Wen-tau Yih and Yejin, Choi},
title = {Abductive Commonsense Reasoning},
year = {2020}
}"""
# TODO(art):
_DESCRIPTION = """\
the Abductive Natural Language Inference Dataset from AI2
"""
_DATA_URL = "https://storage.googleapis.com/ai2-mosaic/public/alphanli/alphanli-train-dev.zip"
class ArtConfig(datasets.BuilderConfig):
"""BuilderConfig for Art."""
def __init__(self, **kwargs):
"""BuilderConfig for Art.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(ArtConfig, self).__init__(version=datasets.Version("0.1.0", ""), **kwargs)
class Art(datasets.GeneratorBasedBuilder):
"""TODO(art): Short description of my dataset."""
# TODO(art): Set up version.
VERSION = datasets.Version("0.1.0")
BUILDER_CONFIGS = [
ArtConfig(
name="anli",
description="""\
the Abductive Natural Language Inference Dataset from AI2.
""",
),
]
def _info(self):
# TODO(art): Specifies the datasets.DatasetInfo object
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=datasets.Features(
{
"observation_1": datasets.Value("string"),
"observation_2": datasets.Value("string"),
"hypothesis_1": datasets.Value("string"),
"hypothesis_2": datasets.Value("string"),
"label": datasets.features.ClassLabel(num_classes=3)
# These are the features of your dataset like images, labels ...
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="https://leaderboard.allenai.org/anli/submissions/get-started",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(art): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
dl_dir = dl_manager.download_and_extract(_DATA_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(dl_dir, "dev.jsonl"),
"labelpath": os.path.join(dl_dir, "dev-labels.lst"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(dl_dir, "train.jsonl"),
"labelpath": os.path.join(dl_dir, "train-labels.lst"),
},
),
]
def _generate_examples(self, filepath, labelpath):
"""Yields examples."""
# TODO(art): Yields (key, example) tuples from the dataset
data = []
for line in open(filepath, encoding="utf-8"):
data.append(json.loads(line))
labels = []
with open(labelpath, encoding="utf-8") as f:
for word in f:
labels.append(word)
for idx, row in enumerate(data):
yield idx, {
"observation_1": row["obs1"],
"observation_2": row["obs2"],
"hypothesis_1": row["hyp1"],
"hypothesis_2": row["hyp2"],
"label": labels[idx],
}
|