Datasets:
totto

Languages: English
Multilinguality: monolingual
Size Categories: 100K<n<1M
Language Creators: found
Annotations Creators: expert-generated
Source Datasets: original
totto / totto.py
system's picture
system
HF staff
Update files from the datasets library (from 1.12.0)
2e57021
# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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
import json
import os
import datasets
_DESCRIPTION = """\
ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
"""
_HOMEPAGE_URL = ""
_URL = "https://storage.googleapis.com/totto-public/totto_data.zip"
_CITATION = """\
@inproceedings{parikh2020totto,
title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},
author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},
booktitle={Proceedings of EMNLP},
year={2020}
}
"""
class Totto(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int32"),
"table_page_title": datasets.Value("string"),
"table_webpage_url": datasets.Value("string"),
"table_section_title": datasets.Value("string"),
"table_section_text": datasets.Value("string"),
"table": [
[
{
"column_span": datasets.Value("int32"),
"is_header": datasets.Value("bool"),
"row_span": datasets.Value("int32"),
"value": datasets.Value("string"),
}
]
],
"highlighted_cells": datasets.Sequence(datasets.Sequence(datasets.Value("int32"))),
"example_id": datasets.Value("string"),
"sentence_annotations": datasets.Sequence(
{
"original_sentence": datasets.Value("string"),
"sentence_after_deletion": datasets.Value("string"),
"sentence_after_ambiguity": datasets.Value("string"),
"final_sentence": datasets.Value("string"),
}
),
"overlap_subset": datasets.Value("string"),
},
),
supervised_keys=None,
homepage=_HOMEPAGE_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
path = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"datapath": os.path.join(path, "totto_data/totto_train_data.jsonl"),
"datatype": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"datapath": os.path.join(path, "totto_data/totto_dev_data.jsonl"),
"datatype": "valid",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"datapath": os.path.join(path, "totto_data/unlabeled_totto_test_data.jsonl"),
"datatype": "test",
},
),
]
def _generate_examples(self, datapath, datatype):
with open(datapath, "r", encoding="utf-8") as json_file:
json_list = list(json_file)
for example_counter, json_str in enumerate(json_list):
result = json.loads(json_str)
response = {
"id": example_counter,
"table_page_title": result["table_page_title"],
"table_webpage_url": result["table_webpage_url"],
"table_section_title": result["table_section_title"],
"table_section_text": result["table_section_text"],
"table": result["table"],
"highlighted_cells": result["highlighted_cells"],
"example_id": str(result["example_id"]),
}
if datatype == "train":
response["overlap_subset"] = "none"
else:
response["overlap_subset"] = str(result["overlap_subset"])
if datatype == "test":
response["sentence_annotations"] = [
{
"original_sentence": "none",
"sentence_after_deletion": "none",
"sentence_after_ambiguity": "none",
"final_sentence": "none",
}
]
else:
response["sentence_annotations"] = result["sentence_annotations"]
yield example_counter, response