Datasets:
Sub-tasks:
dialogue-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
no-annotation
Source Datasets:
original
License:
# coding=utf-8 | |
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
# | |
# 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. | |
# Lint as: python3 | |
"""CRD3 dataset""" | |
import json | |
import os | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
_CITATION = """ | |
@inproceedings{ | |
title = {Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset}, | |
author = {Rameshkumar, Revanth and Bailey, Peter}, | |
year = {2020}, | |
publisher = {Association for Computational Linguistics}, | |
conference = {ACL} | |
} | |
""" | |
_DESCRIPTION = """ | |
Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset. | |
Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game. | |
The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding | |
abstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player | |
collaboration and spoken interaction. For each dialogue, there are a large number of turns, multiple abstractive summaries with varying levels of detail, | |
and semantic ties to the previous dialogues. | |
""" | |
_URL = "https://huggingface.co/datasets/crd3/resolve/72bffe55b4d5bf19b530d3e417447b3384ba3673/data/aligned%20data.zip" | |
def get_train_test_dev_files(files, test_split, train_split, dev_split): | |
test_files, dev_files, train_files = [], [], [] | |
for file in files: | |
filename = os.path.split(file)[1].split("_")[0] | |
if filename in test_split: | |
test_files.append(file) | |
elif filename in train_split: | |
train_files.append(file) | |
elif filename in dev_split: | |
dev_files.append(file) | |
else: | |
logger.info(f"skipped file {file}") | |
return test_files, train_files, dev_files | |
class CRD3(datasets.GeneratorBasedBuilder): | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"chunk": datasets.Value("string"), | |
"chunk_id": datasets.Value("int32"), | |
"turn_start": datasets.Value("int32"), | |
"turn_end": datasets.Value("int32"), | |
"alignment_score": datasets.Value("float32"), | |
"turns": [ | |
{ | |
"names": datasets.features.Sequence(datasets.Value("string")), | |
"utterances": datasets.features.Sequence(datasets.Value("string")), | |
"number": datasets.Value("int32"), | |
} | |
], | |
} | |
), | |
homepage="https://github.com/RevanthRameshkumar/CRD3", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
root = dl_manager.download_and_extract(_URL) | |
path = os.path.join(root, "aligned data") | |
test_file = os.path.join(path, "test_files") | |
train_file = os.path.join(path, "train_files") | |
dev_file = os.path.join(path, "val_files") | |
with open(test_file, encoding="utf-8") as f: | |
test_splits = [file.replace("\n", "") for file in f.readlines()] | |
with open(train_file, encoding="utf-8") as f: | |
train_splits = [file.replace("\n", "") for file in f.readlines()] | |
with open(dev_file, encoding="utf-8") as f: | |
dev_splits = [file.replace("\n", "") for file in f.readlines()] | |
c2 = "c=2" | |
c3 = "c=3" | |
c4 = "c=4" | |
files = [os.path.join(path, c2, file) for file in sorted(os.listdir(os.path.join(path, c2)))] | |
files.extend([os.path.join(path, c3, file) for file in sorted(os.listdir(os.path.join(path, c3)))]) | |
files.extend([os.path.join(path, c4, file) for file in sorted(os.listdir(os.path.join(path, c4)))]) | |
test_files, train_files, dev_files = get_train_test_dev_files(files, test_splits, train_splits, dev_splits) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={"files_path": train_files}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={"files_path": test_files}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={"files_path": dev_files}, | |
), | |
] | |
def _generate_examples(self, files_path): | |
"""Yields examples.""" | |
for id0, file in enumerate(files_path): | |
with open(file, encoding="utf-8") as f: | |
data = json.load(f) | |
for id1, row in enumerate(data): | |
chunk = row["CHUNK"] | |
chunk_id = row["ALIGNMENT"]["CHUNK ID"] | |
turn_start = row["ALIGNMENT"]["TURN START"] | |
turn_end = row["ALIGNMENT"]["TURN END"] | |
score = row["ALIGNMENT"]["ALIGNMENT SCORE"] | |
for turn in row["TURNS"]: | |
turn["names"] = turn["NAMES"] | |
turn["utterances"] = turn["UTTERANCES"] | |
turn["number"] = turn["NUMBER"] | |
del turn["NAMES"] | |
del turn["UTTERANCES"] | |
del turn["NUMBER"] | |
yield str(id0) + "_" + str(id1), { | |
"chunk": chunk, | |
"chunk_id": chunk_id, | |
"turn_start": turn_start, | |
"turn_end": turn_end, | |
"alignment_score": score, | |
"turns": row["TURNS"], | |
} | |