Delete FIREBALL.py

FIREBALL.py

@@ -1,236 +0,0 @@
-# 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/filtered",
-}
-
-
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class NewDataset(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
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
-    ]
-
-    DEFAULT_CONFIG_NAME = "FIREBALL"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    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):
-    #     # 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
-    #     urls = _URLS[self.config.name]
-    #     data_dir = dl_manager.download_and_extract(urls)
-    #     for root,dirs,files in os.walk(data_dir):
-    #         for f in files:
-    #             data = os.path.join()
-    #     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",
-    #             },
-    #         ),
-    #     ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # 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 root,dirs,files in os.walk(filepath):
-          for file in files:
-            with jsonlines.open(os.path.join(root,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