# 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