Hugging Face's logo

Datasets:
GEM
/
totto

Tasks:
Table to Text
Languages: English
Multilinguality: unknown
Size Categories: unknown
Language Creators: unknown
Annotations Creators: none
Source Datasets: original
ArXiv:
Tags: data-to-text
License:
Dataset card Viewer Files Community
4
totto / totto.py
Sebastian Gehrmann
linearization
cbc53cf
raw history blame contribute delete
No virus
13.2 kB
import copy
import json
import os
import datasets
_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}
}
"""
_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.
"""
_URLs = {
"totto": {
"data": "https://storage.googleapis.com/totto-public/totto_data.zip",
"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/totto.zip",
},
}
def _add_adjusted_col_offsets(table):
"""Add adjusted column offsets to take into account multi-column cells."""
adjusted_table = []
for row in table:
real_col_index = 0
adjusted_row = []
for cell in row:
adjusted_cell = copy.deepcopy(cell)
adjusted_cell["adjusted_col_start"] = real_col_index
adjusted_cell["adjusted_col_end"] = (
adjusted_cell["adjusted_col_start"] + adjusted_cell["column_span"]
)
real_col_index += adjusted_cell["column_span"]
adjusted_row.append(adjusted_cell)
adjusted_table.append(adjusted_row)
return adjusted_table
def _get_heuristic_row_headers(adjusted_table, row_index, col_index):
"""Heuristic to find row headers."""
row_headers = []
row = adjusted_table[row_index]
for i in range(0, col_index):
if row[i]["is_header"]:
row_headers.append(row[i])
return row_headers
def _get_heuristic_col_headers(adjusted_table, row_index, col_index):
"""Heuristic to find column headers."""
adjusted_cell = adjusted_table[row_index][col_index]
adjusted_col_start = adjusted_cell["adjusted_col_start"]
adjusted_col_end = adjusted_cell["adjusted_col_end"]
col_headers = []
for r in range(0, row_index):
row = adjusted_table[r]
for cell in row:
if (
cell["adjusted_col_start"] < adjusted_col_end
and cell["adjusted_col_end"] > adjusted_col_start
):
if cell["is_header"]:
col_headers.append(cell)
return col_headers
def get_highlighted_subtable(table, cell_indices, with_heuristic_headers=False):
"""Extract out the highlighted part of a table."""
highlighted_table = []
adjusted_table = _add_adjusted_col_offsets(table)
for (row_index, col_index) in cell_indices:
cell = table[row_index][col_index]
if with_heuristic_headers:
row_headers = _get_heuristic_row_headers(
adjusted_table, row_index, col_index
)
col_headers = _get_heuristic_col_headers(
adjusted_table, row_index, col_index
)
else:
row_headers = []
col_headers = []
highlighted_cell = {
"cell": cell,
"row_headers": row_headers,
"col_headers": col_headers,
}
highlighted_table.append(highlighted_cell)
return highlighted_table
def linearize_subtable(subtable, table_page_title, table_section_title):
"""Linearize the highlighted subtable and return a string of its contents."""
table_str = ""
if table_page_title:
table_str += "<page_title> " + table_page_title + " </page_title> "
if table_section_title:
table_str += "<section_title> " + table_section_title + " </section_title> "
table_str += "<table> "
for item in subtable:
cell = item["cell"]
row_headers = item["row_headers"]
col_headers = item["col_headers"]
# The value of the cell.
item_str = "<cell> " + cell["value"] + " "
# All the column headers associated with this cell.
for col_header in col_headers:
item_str += "<col_header> " + col_header["value"] + " </col_header> "
# All the row headers associated with this cell.
for row_header in row_headers:
item_str += "<row_header> " + row_header["value"] + " </row_header> "
item_str += "</cell> "
table_str += item_str
table_str += "</table>"
return table_str
def linearize(example):
table = example["table"]
table_page_title = example["table_page_title"]
table_section_title = example["table_section_title"]
cell_indices = example["highlighted_cells"]
subtable = get_highlighted_subtable(
table=table, cell_indices=cell_indices, with_heuristic_headers=True
)
subtable_metadata_str = linearize_subtable(
subtable=subtable,
table_page_title=table_page_title,
table_section_title=table_section_title,
)
return subtable_metadata_str
class Totto(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="totto",
version=datasets.Version("1.0.0"),
description=f"GEM benchmark: struct2text task",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"gem_id": datasets.Value("string"),
"gem_parent_id": datasets.Value("string"),
"totto_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.Value("int32")]],
"example_id": datasets.Value("string"),
"sentence_annotations": [
{
"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"),
"target": datasets.Value("string"), # single target for train
"references": [datasets.Value("string")],
"linearized_input": datasets.Value("string"),
},
),
supervised_keys=None,
homepage="",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_dir = dl_manager.download_and_extract(_URLs[self.config.name])
challenge_sets = [
("challenge_train_sample", "train_totto_RandomSample500.json"),
("challenge_validation_sample", "validation_totto_RandomSample500.json"),
# ("challenge_test_scramble", "test_totto_ScrambleInputStructure500.json"),
]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(
dl_dir["data"], "totto_data/totto_train_data.jsonl"
),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(
dl_dir["data"], "totto_data/totto_dev_data.jsonl"
),
"split": "validation",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(
dl_dir["data"], "totto_data/unlabeled_totto_test_data.jsonl"
),
"split": "test",
},
),
] + [
datasets.SplitGenerator(
name=challenge_split,
gen_kwargs={
"filepath": os.path.join(
dl_dir["challenge_set"], self.config.name, filename
),
"split": challenge_split,
},
)
for challenge_split, filename in challenge_sets
]
def _generate_examples(self, filepath, split, filepaths=None, lang=None):
"""Yields examples."""
if "challenge" in split:
exples = json.load(open(filepath, encoding="utf-8"))
if isinstance(exples, dict):
assert len(exples) == 1, "multiple entries found"
exples = list(exples.values())[0]
for id_, exple in enumerate(exples):
if len(exple) == 0:
continue
exple["gem_parent_id"] = exple["gem_id"]
exple["gem_id"] = f"{self.config.name}-{split}-{id_}"
exple["linearized_input"] = linearize(exple)
yield id_, exple
else:
with open(filepath, "r", encoding="utf-8") as json_file:
json_list = list(json_file)
id_ = -1
i = -1
for json_str in json_list:
result = json.loads(json_str)
linearized_input = linearize(result)
if split == "train":
i += 1
for sentence in result["sentence_annotations"]:
id_ += 1
response = {
"gem_id": f"{self.config.name}-{split}-{id_}",
"gem_parent_id": f"{self.config.name}-{split}-{id_}",
"totto_id": i,
"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"]),
"overlap_subset": "none",
"sentence_annotations": [sentence],
"references": [sentence["final_sentence"]],
"target": sentence["final_sentence"],
"linearized_input": linearized_input,
}
yield id_, response
else:
id_ += 1
response = {
"gem_id": f"{self.config.name}-{split}-{id_}",
"gem_parent_id": f"{self.config.name}-{split}-{id_}",
"totto_id": id_,
"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"]),
"overlap_subset": str(result["overlap_subset"]),
"linearized_input": linearized_input,
}
response["sentence_annotations"] = (
[] if split == "test" else result["sentence_annotations"]
)
response["references"] = [
sentence["final_sentence"]
for sentence in response["sentence_annotations"]
]
response["target"] = (
response["references"][0]
if len(response["references"]) > 0
else ""
)
yield id_, response