| # 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 | |
| # TODO: Add BibTeX citation | |
| # Find for instance the citation on arxiv or on the dataset repo/website | |
| _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" | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = "cc-by-4.0" | |
| # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. | |
| # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
| _URLS = { | |
| "FIREBALL": "https://huggingface.co/datasets/lara-martin/FIREBALL/tree/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 = datasets.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 | |
| #data_dir = dl_manager.download_and_extract(urls) | |
| 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]) | |
| 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): | |
| # TODO: 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 | |