Datasets:
Tasks:
Table to Text
Modalities:
Text
Languages:
English
Size:
10K - 100K
Tags:
data-to-text
License:
File size: 12,466 Bytes
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""TODO: Add a description here."""
import csv
import json
import os
import datasets
_CITATION = """\
@inproceedings{puduppully-etal-2019-data,
title = "Data-to-text Generation with Entity Modeling",
author = "Puduppully, Ratish and
Dong, Li and
Lapata, Mirella",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1195",
doi = "10.18653/v1/P19-1195",
pages = "2023--2035",
}
"""
_DESCRIPTION = """\
The MLB dataset for data to text generation contains Major League Baseball games statistics and
their human-written summaries.
"""
_HOMEPAGE = "https://github.com/ratishsp/mlb-data-scripts"
_LICENSE = ""
_URL = "data.tar.bz2"
class MlbDataToText(datasets.GeneratorBasedBuilder):
"""MLB dataset for data to text generation"""
VERSION = datasets.Version("1.1.0")
def _info(self):
features = datasets.Features(
{
"home_name": datasets.Value("string"),
"box_score": [
{
"p_l": datasets.Value("string"),
"last_name": datasets.Value("string"),
"p_h": datasets.Value("string"),
"sac": datasets.Value("string"),
"p_bb": datasets.Value("string"),
"pos": datasets.Value("string"),
"ao": datasets.Value("string"),
"p_bf": datasets.Value("string"),
"cs": datasets.Value("string"),
"hbp": datasets.Value("string"),
"ab": datasets.Value("string"),
"full_name": datasets.Value("string"),
"p_w": datasets.Value("string"),
"go": datasets.Value("string"),
"fldg": datasets.Value("string"),
"p_bs": datasets.Value("string"),
"avg": datasets.Value("string"),
"p_r": datasets.Value("string"),
"p_s": datasets.Value("string"),
"lob": datasets.Value("string"),
"first_name": datasets.Value("string"),
"p_sv": datasets.Value("string"),
"p_so": datasets.Value("string"),
"p_save": datasets.Value("string"),
"p_hr": datasets.Value("string"),
"po": datasets.Value("string"),
"p_ip1": datasets.Value("string"),
"p_ip2": datasets.Value("string"),
"bb": datasets.Value("string"),
"ops": datasets.Value("string"),
"p_hld": datasets.Value("string"),
"bo": datasets.Value("string"),
"p_loss": datasets.Value("string"),
"e": datasets.Value("string"),
"p_game_score": datasets.Value("string"),
"p_win": datasets.Value("string"),
"a": datasets.Value("string"),
"p_era": datasets.Value("string"),
"d": datasets.Value("string"),
"p_out": datasets.Value("string"),
"h": datasets.Value("string"),
"p_er": datasets.Value("string"),
"p_np": datasets.Value("string"),
"hr": datasets.Value("string"),
"r": datasets.Value("string"),
"so": datasets.Value("string"),
"t": datasets.Value("string"),
"rbi": datasets.Value("string"),
"team": datasets.Value("string"),
"sb": datasets.Value("string"),
"slg": datasets.Value("string"),
"sf": datasets.Value("string"),
"obp": datasets.Value("string"),
}
],
"home_city": datasets.Value("string"),
"vis_name": datasets.Value("string"),
"play_by_play": [{
"top": [{
"runs": datasets.Value("string"),
"scorers": [
datasets.Value("string")
],
"pitcher": datasets.Value("string"),
"o": datasets.Value("string"),
"b": datasets.Value("string"),
"s": datasets.Value("string"),
"batter": datasets.Value("string"),
"b1": [
datasets.Value("string")
],
"b2": [
datasets.Value("string")
],
"b3": [
datasets.Value("string")
],
"event": datasets.Value("string"),
"event2": datasets.Value("string"),
"home_team_runs": datasets.Value("string"),
"away_team_runs": datasets.Value("string"),
"rbi": datasets.Value("string"),
"error_runs": datasets.Value("string"),
"fielder_error": datasets.Value("string")
}
],
"bottom": [{
"runs": datasets.Value("string"),
"scorers": [
datasets.Value("string")
],
"pitcher": datasets.Value("string"),
"o": datasets.Value("string"),
"b": datasets.Value("string"),
"s": datasets.Value("string"),
"batter": datasets.Value("string"),
"b1": [
datasets.Value("string")
],
"b2": [
datasets.Value("string")
],
"b3": [
datasets.Value("string")
],
"event": datasets.Value("string"),
"event2": datasets.Value("string"),
"home_team_runs": datasets.Value("string"),
"away_team_runs": datasets.Value("string"),
"rbi": datasets.Value("string"),
"error_runs": datasets.Value("string"),
"fielder_error": datasets.Value("string")
}
],
"inning": datasets.Value("string")
}
],
"vis_line": {
"innings": [{
"inn": datasets.Value("string"),
"runs": datasets.Value("string")
}
],
"result": datasets.Value("string"),
"team_runs": datasets.Value("string"),
"team_hits": datasets.Value("string"),
"team_errors": datasets.Value("string"),
"team_name": datasets.Value("string"),
"team_city": datasets.Value("string")
},
"home_line": {
"innings": [{
"inn": datasets.Value("string"),
"runs": datasets.Value("string")
}
],
"result": datasets.Value("string"),
"team_runs": datasets.Value("string"),
"team_hits": datasets.Value("string"),
"team_errors": datasets.Value("string"),
"team_name": datasets.Value("string"),
"team_city": datasets.Value("string")
},
"vis_city": datasets.Value("string"),
"day": datasets.Value("string"),
"summary": [
datasets.Value("string"),
],
"gem_id": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
data_dir = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "train.jsonl"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "test.jsonl"),
"split": "test"
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "validation.jsonl"),
"split": "validation",
},
),
]
def _generate_examples(
self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
):
""" Yields examples as (key, example) tuples. """
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is here for legacy reason (tfds) and is not important in itself.
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
yield id_, {
"home_name": data["home_name"],
"box_score": data["box_score"],
"home_city": data["home_city"],
"vis_name": data["vis_name"],
"play_by_play": data["play_by_play"],
"vis_line": data["vis_line"],
"vis_city": data["vis_city"],
"day": data["day"],
"home_line": data["home_line"],
"summary": data["summary"],
"gem_id": data["gem_id"]
}
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