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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: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
import csv
import json
import jsonlines
import os
import datasets
from datasets import Features
_CITATION = """\
@inproceedings{Zhu2023FIREBALL,
title={{FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information}},
author={Zhu, Andrew and Aggarwal, Karmanya and Feng, Alexander and Martin, Lara J. and Callison-Burch, Chris},
year={2023},
booktitle={Annual Meeting of the Association for Computational Linguistics (ACL)},
month={7},
url={https://aclanthology.org/2023.acl-long.229/},
address={Toronto, Canada},
pages={4171--4193},
publisher={ACL},
doi={10.18653/v1/2023.acl-long.229}
}
"""
_DESCRIPTION = """\
FIREBALL Dungeons & Dragons data with narrative and Avrae scripting commands.
"""
_HOMEPAGE = "https://github.com/zhudotexe/FIREBALL"
_LICENSE = "cc-by-4.0"
_URLS = {
"FIREBALL": "https://huggingface.co/datasets/lara-martin/FIREBALL/raw/main/"
}
class Fireball(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.0.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="FIREBALL", version=VERSION),
]
def _info(self):
features = Features(
{
"speaker_id": datasets.Value('int64'),
"before_utterances": datasets.Sequence(datasets.Value('string')),
'combat_state_before': datasets.Sequence(
{
'name': datasets.Value(dtype='string'),
'hp': datasets.Value(dtype='string'),
'class': datasets.Value(dtype='string'),
'race': datasets.Value(dtype='string'),
'attacks': datasets.Value(dtype='string'),
'spells': datasets.Value(dtype='string'),
'actions': datasets.Value(dtype='string'),
'effects': datasets.Value(dtype='string'),
'description': datasets.Value(dtype='string'),
'controller_id': datasets.Value(dtype='string')
}
), #list of dictionaries
'current_actor': {
'name': datasets.Value(dtype='string'),
'hp': datasets.Value(dtype='string'),
'class': datasets.Value(dtype='string'),
'race': datasets.Value(dtype='string'),
'attacks': datasets.Value(dtype='string'),
'spells': datasets.Value(dtype='string'),
'actions': datasets.Value(dtype='string'),
'effects': datasets.Value(dtype='string'),
'description': datasets.Value(dtype='string'),
'controller_id': datasets.Value(dtype='string')
}, #dictionary
'commands_norm': datasets.Value('string'),
'automation_results': datasets.Value('string'),
'caster_after': {
'name': datasets.Value(dtype='string'),
'hp': datasets.Value(dtype='string'),
'class': datasets.Value(dtype='string'),
'race': datasets.Value(dtype='string'),
'attacks': datasets.Value(dtype='string'),
'spells': datasets.Value(dtype='string'),
'actions': datasets.Value(dtype='string'),
'effects': datasets.Value(dtype='string'),
'description': datasets.Value(dtype='string'),
'controller_id': datasets.Value(dtype='string')
}, #dictionary
'targets_after': datasets.Sequence(
{
'name': datasets.Value(dtype='string'),
'hp': datasets.Value(dtype='string'),
'class': datasets.Value(dtype='string'),
'race': datasets.Value(dtype='string'),
'attacks': datasets.Value(dtype='string'),
'spells': datasets.Value(dtype='string'),
'actions': datasets.Value(dtype='string'),
'effects': datasets.Value(dtype='string'),
'description': datasets.Value(dtype='string'),
'controller_id': datasets.Value(dtype='string')
}
), #list of dictionaries
'combat_state_after': datasets.Sequence(
{
'name': datasets.Value(dtype='string'),
'hp': datasets.Value(dtype='string'),
'class': datasets.Value(dtype='string'),
'race': datasets.Value(dtype='string'),
'attacks': datasets.Value(dtype='string'),
'spells': datasets.Value(dtype='string'),
'actions': datasets.Value(dtype='string'),
'effects': datasets.Value(dtype='string'),
'description': datasets.Value(dtype='string'),
'controller_id': datasets.Value(dtype='string')
}
), #list of dictionaries
'after_utterances': datasets.Sequence(datasets.Value('string')),
'utterance_history': datasets.Sequence(datasets.Value('string')),
'before_idxs': datasets.Sequence(datasets.Value('int16')),
'before_state_idx': datasets.Value('int16'),
'command_idxs': datasets.Sequence(datasets.Value('int16')),
'after_state_idx': datasets.Value('int16'),
'after_idxs': datasets.Sequence(datasets.Value('int16')),
'embed_idxs': datasets.Sequence(datasets.Value('int16'))
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# based off of OSCAR - https://huggingface.co/datasets/oscar/blob/main/oscar.py
url = _URLS[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
file_list = dl_manager.download(url+"files.txt")
with open(file_list) as f:
data_filenames = [line.strip() for line in f if line]
data_urls = dl_manager.download([url+"filtered/"+data_filename for data_filename in data_filenames])
# data_urls = dl_manager.download([url+"filtered/00068c6b03adc2c102756053cf6edd05.jsonl"])
downloaded_files = dl_manager.download(data_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath):
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
key = 0
for file in filepath:
with jsonlines.open(file) as f:
for data in f:
# Yields examples as (key, example) tuples
yield key, {
"speaker_id": data["speaker_id"],
"before_utterances": data["before_utterances"],
'combat_state_before': data['combat_state_before'],
'current_actor': data["current_actor"],
'commands_norm': data['commands_norm'],
'automation_results': data['automation_results'],
'caster_after': data['caster_after'],
'targets_after': data['targets_after'],
'combat_state_after': data['combat_state_after'],
'after_utterances': data['after_utterances'],
'utterance_history': data['utterance_history'],
'before_idxs': data['before_idxs'],
'before_state_idx': data['before_state_idx'],
'command_idxs': data['command_idxs'],
'after_state_idx': data['after_state_idx'],
'after_idxs': data['after_idxs'],
'embed_idxs': data['embed_idxs']
}
key+=1
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