# coding=utf-8 # 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. """Movie Dialog Dataset.""" import datasets _CITATION = """\ @misc{dodge2016evaluating, title={Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems}, author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston}, year={2016}, eprint={1511.06931}, archivePrefix={arXiv}, primaryClass={cs.CL} } """ _DESCRIPTION = """\ The Movie Dialog dataset (MDD) is designed to measure how well models can perform at goal and non-goal orientated dialog centered around the topic of movies (question answering, recommendation and discussion). """ _HOMEPAGE = "https://research.fb.com/downloads/babi/" _LICENSE = """Creative Commons Attribution 3.0 License""" ZIP_URL = "http://www.thespermwhale.com/jaseweston/babi/movie_dialog_dataset.tgz" REDDIT_URL = "http://tinyurl.com/p6tyohj" dir = "movie_dialog_dataset/" dir2 = "" paths = { "task1_qa": { "train": dir + "task1_qa/task1_qa_train.txt", "dev": dir + "task1_qa/task1_qa_dev.txt", "test": dir + "task1_qa/task1_qa_test.txt", }, "task2_recs": { "train": dir + "task2_recs/task2_recs_train.txt", "dev": dir + "task2_recs/task2_recs_dev.txt", "test": dir + "task2_recs/task2_recs_test.txt", }, "task3_qarecs": { "train": dir + "task3_qarecs/task3_qarecs_train.txt", "dev": dir + "task3_qarecs/task3_qarecs_dev.txt", "test": dir + "task3_qarecs/task3_qarecs_test.txt", }, "task4_reddit": { "train": "task4_reddit/task4_reddit_train.txt", "dev": "task4_reddit/task4_reddit_dev.txt", "test": "task4_reddit/task4_reddit_test.txt", "cand_valid": "task4_reddit/task4_reddit_cand-valid.txt", "cand_test": "task4_reddit/task4_reddit_cand-test.txt", }, } class Mdd(datasets.GeneratorBasedBuilder): """The Movie Dialog Dataset""" VERSION = datasets.Version("1.1.0") BUILDER_CONFIGS = [ datasets.BuilderConfig( name="task1_qa", version=VERSION, description="This part of my dataset covers task1_qa part of the dataset" ), datasets.BuilderConfig( name="task2_recs", version=VERSION, description="This part of my dataset covers task2_recs part of the dataset", ), datasets.BuilderConfig( name="task3_qarecs", version=VERSION, description="This part of my dataset covers task3_qarecs part of the dataset", ), datasets.BuilderConfig( name="task4_reddit", version=VERSION, description="This part of my dataset covers task4_reddit part of the dataset", ), ] def _info(self): features = datasets.Features( { "dialogue_turns": datasets.Sequence( { "speaker": datasets.Value("int32"), "utterance": datasets.Value("string"), } ), } ) 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, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # 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): """Returns SplitGenerators.""" if self.config.name != "task4_reddit": my_urls = ZIP_URL # Cannot download just one single type as it is a compressed file. else: my_urls = REDDIT_URL archive = dl_manager.download(my_urls) splits = [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": paths[self.config.name]["train"], "files": dl_manager.iter_archive(archive)}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": paths[self.config.name]["test"], "files": dl_manager.iter_archive(archive)}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": paths[self.config.name]["dev"], "files": dl_manager.iter_archive(archive)}, ), ] if self.config.name == "task4_reddit": splits += [ datasets.SplitGenerator( name=datasets.Split("cand_valid"), # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": paths[self.config.name]["cand_valid"], "files": dl_manager.iter_archive(archive), }, ), datasets.SplitGenerator( name=datasets.Split("cand_test"), # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": paths[self.config.name]["cand_test"], "files": dl_manager.iter_archive(archive), }, ), ] return splits def _generate_examples(self, filepath, files): for path, f in files: if path == filepath: if "cand" not in filepath: dialogue_turns = [] example_idx = 0 for idx, line in enumerate(f): line = line.decode("utf-8") if line.strip() == "": if dialogue_turns != []: yield example_idx, {"dialogue_turns": dialogue_turns} example_idx += 1 dialogue_turns = [] elif line.strip().split()[0] == "1": # New convo if dialogue_turns != []: # Already some convo, flush it out yield example_idx, {"dialogue_turns": dialogue_turns} example_idx += 1 dialogue_turns = [] exchange = line[len(line.split()[0]) :].strip().split("\t") # Skip the number in the front sp1 = exchange[0] sp2 = exchange[-1] # Might contain multiple tabs in between. dialogue_turns.append({"speaker": 0, "utterance": sp1}) dialogue_turns.append({"speaker": 1, "utterance": sp2}) else: exchange = line[len(line.split()[0]) :].strip().split("\t") # Skip the number in the front sp1 = exchange[0] sp2 = exchange[-1] # Might contain multiple tabs in between. dialogue_turns.append({"speaker": 0, "utterance": sp1}) dialogue_turns.append({"speaker": 1, "utterance": sp2}) else: if dialogue_turns != []: yield example_idx, {"dialogue_turns": dialogue_turns} else: dialogue_turns = [] example_idx = 0 for idx, line in enumerate(f): line = line.decode("utf-8") if line.strip() == "": if dialogue_turns != []: yield example_idx, {"dialogue_turns": dialogue_turns} example_idx += 1 dialogue_turns = [] elif line.strip().split()[0] == "1": # New convo if dialogue_turns != []: # Already some convo, flush it out yield example_idx, {"dialogue_turns": dialogue_turns} example_idx += 1 dialogue_turns = [] exchange = line[len(line.split()[0]) :].strip() # Skip the number in the front sp1 = exchange dialogue_turns.append({"speaker": 0, "utterance": sp1}) else: exchange = line[len(line.split()[0]) :].strip() # Skip the number in the front sp1 = exchange dialogue_turns.append({"speaker": 0, "utterance": sp1}) else: # Last line, new example if dialogue_turns != []: yield example_idx, {"dialogue_turns": dialogue_turns} break