Datasets:
Tasks:
Table to Text
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
# 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 | |