File size: 5,774 Bytes
f0a485a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
# -*- coding: utf-8 -*-
"""CLUTRR_Dataset Loading Script.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1q9DdeHA5JbgTHkH6kfZe_KWHQOwHZA97
"""
# coding=utf-8
# Copyright 2019 The CLUTRR Datasets Authors and the HuggingFace Datasets Authors.
#
# CLUTRR is CC-BY-NC 4.0 (Attr Non-Commercial Inter.) licensed, as found in the LICENSE file.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""The CLUTRR (Compositional Language Understanding and Text-based Relational Reasoning) benchmark."""
import csv
import os
import textwrap
import numpy as np
import datasets
import json
_CLUTRR_CITATION = """\
@article{sinha2019clutrr,
Author = {Koustuv Sinha and Shagun Sodhani and Jin Dong and Joelle Pineau and William L. Hamilton},
Title = {CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text},
Year = {2019},
journal = {Empirical Methods of Natural Language Processing (EMNLP)},
arxiv = {1908.06177}
}
"""
_CLUTRR_DESCRIPTION = """\
CLUTRR (Compositional Language Understanding and Text-based Relational Reasoning),
a diagnostic benchmark suite, is first introduced in (https://arxiv.org/abs/1908.06177)
to test the systematic generalization and inductive reasoning capabilities of NLU systems.
"""
_URL = "https://raw.githubusercontent.com/kliang5/CLUTRR_huggingface_dataset/main/"
_TASK = ["gen_train23_test2to10", "gen_train234_test2to10", "rob_train_clean_23_test_all_23", "rob_train_disc_23_test_all_23", "rob_train_irr_23_test_all_23","rob_train_sup_23_test_all_23"]
class v1(datasets.GeneratorBasedBuilder):
"""BuilderConfig for CLUTRR."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=task,
version=datasets.Version("1.0.0"),
description="",
)
for task in _TASK
]
def _info(self):
return datasets.DatasetInfo(
description=_CLUTRR_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"story": datasets.Value("string"),
"query": datasets.Value("string"),
"target": datasets.Value("int32"),
"target_text": datasets.Value("string"),
"clean_story": datasets.Value("string"),
"proof_state": datasets.Value("string"),
"f_comb": datasets.Value("string"),
"task_name": datasets.Value("string"),
"story_edges": datasets.Value("string"),
"edge_types": datasets.Value("string"),
"query_edge": datasets.Value("string"),
"genders": datasets.Value("string"),
"task_split": datasets.Value("string"),
}
),
# No default supervised_keys (as we have to pass both premise
# and hypothesis as input).
supervised_keys=None,
homepage="https://www.cs.mcgill.ca/~ksinha4/clutrr/",
citation=_CLUTRR_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
task = str(self.config.name)
urls_to_download = {
"test": _URL + task + "/test.csv",
"train": _URL + task + "/train.csv",
"validation": _URL + task + "/validation.csv",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["train"],
"task": task,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["validation"],
"task": task,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["test"],
"task": task,
},
),
]
def _generate_examples(self, filepath, task):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
reader = csv.reader(f)
for id_, data in enumerate(reader):
if id_ == 0:
continue
# yield id_, data
# id_ += 1
yield id_, {
"id": data[1],
"story": data[2],
"query": data[3],
"target": data[4],
"target_text": data[5],
"clean_story": data[6],
"proof_state": data[7],
"f_comb": data[8],
"task_name": data[9],
"story_edges": data[10],
"edge_types": data[11],
"query_edge": data[12],
"genders": data[13],
"task_split": data[14],
}
|