"""TODO(cornell_movie_dialog): Add a description here.""" import ast import os import datasets # TODO(cornell_movie_dialog): BibTeX citation _CITATION = """\ @InProceedings{Danescu-Niculescu-Mizil+Lee:11a, author={Cristian Danescu-Niculescu-Mizil and Lillian Lee}, title={Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs.}, booktitle={Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, ACL 2011}, year={2011} } """ # TODO(cornell_movie_dialog): _DESCRIPTION = """\ This corpus contains a large metadata-rich collection of fictional conversations extracted from raw movie scripts: - 220,579 conversational exchanges between 10,292 pairs of movie characters - involves 9,035 characters from 617 movies - in total 304,713 utterances - movie metadata included: - genres - release year - IMDB rating - number of IMDB votes - IMDB rating - character metadata included: - gender (for 3,774 characters) - position on movie credits (3,321 characters) """ _URL = "https://www.cs.cornell.edu/~cristian/data/cornell_movie_dialogs_corpus.zip" class CornellMovieDialog(datasets.GeneratorBasedBuilder): """TODO(cornell_movie_dialog): Short description of my dataset.""" # TODO(cornell_movie_dialog): Set up version. VERSION = datasets.Version("0.1.0") def _info(self): # TODO(cornell_movie_dialog): Specifies the datasets.DatasetInfo object return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # datasets.features.FeatureConnectors features=datasets.Features( { "movieID": datasets.Value("string"), "movieTitle": datasets.Value("string"), "movieYear": datasets.Value("string"), "movieIMDBRating": datasets.Value("string"), "movieNoIMDBVotes": datasets.Value("string"), "movieGenres": datasets.features.Sequence(datasets.Value("string")), "characterID1": datasets.Value("string"), "characterID2": datasets.Value("string"), "characterName1": datasets.Value("string"), "characterName2": datasets.Value("string"), "utterance": datasets.features.Sequence( {"text": datasets.Value("string"), "LineID": datasets.Value("string")} ) # These are the features of your dataset like images, labels ... } ), # 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="http://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html", citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" # TODO(cornell_movie_dialog): Downloads the data and defines the splits # dl_manager is a datasets.download.DownloadManager that can be used to # download and extract URLs dl_dir = dl_manager.download_and_extract(_URL) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={"filepaths": os.path.join(dl_dir, "cornell movie-dialogs corpus")}, ), ] def _generate_examples(self, filepaths): """Yields examples.""" # TODO(cornell_movie_dialog): Yields (key, example) tuples from the dataset movie_char_file = os.path.join(filepaths, "movie_characters_metadata.txt") movie_conv_file = os.path.join(filepaths, "movie_conversations.txt") movie_lines_file = os.path.join(filepaths, "movie_lines.txt") movie_titles_file = os.path.join(filepaths, "movie_titles_metadata.txt") with open(movie_char_file, "rb") as f: movie_char_data = [x.decode("latin").split("+++$+++") for x in f.readlines()] with open(movie_conv_file, "rb") as f: movie_conv_data = [x.decode("latin").split("+++$+++") for x in f.readlines()] with open(movie_lines_file, "rb") as f: movie_lines_data = [x.decode("latin").split("+++$+++") for x in f.readlines()] with open(movie_titles_file, "rb") as f: movie_titles_data = [x.decode("latin").split("+++$+++") for x in f.readlines()] # looping over movie conversation file for id_, conv in enumerate(movie_conv_data): char_id_1 = conv[0] char_id_2 = conv[1] movie_id = conv[2] line_ids = conv[-1].replace("\n", "") line_ids = ast.literal_eval(line_ids.strip()) lines_texts = [] # searching text corresponding to each lineID in line_ids in movie lines file for line_id in line_ids: i = 0 while i < len(movie_lines_data) and movie_lines_data[i][0].strip() != line_id: i += 1 lines_texts.append(movie_lines_data[i][0]) # if i < len(movie_lines_data) else '') # look for char names in movie character file j = 0 while j < len(movie_char_data) and movie_char_data[j][0].strip() != char_id_1.strip(): j += 1 char_name_1 = movie_char_data[j][1] # if j < len(movie_char_data) else '' movie_title = movie_char_data[j][3] # if j < len(movie_char_data) else '' k = 0 while k < len(movie_char_data) and movie_char_data[k][0].strip() != char_id_2.strip(): k += 1 char_name_2 = movie_char_data[k][1] # look for movie year, IMDBRating, genre, no_imdb_voting in movie tiles file li = 0 while li < len(movie_titles_data) and movie_titles_data[li][0].strip() != movie_id.strip(): li += 1 movie_year = movie_titles_data[li][2] imdb_rating = movie_titles_data[li][3] no_imdb_vote = movie_titles_data[li][4] genre = movie_titles_data[li][5].replace("\n", "").strip() movie_genres = ast.literal_eval(genre) yield id_, { "movieID": movie_id, "movieTitle": movie_title, "movieYear": movie_year, "movieIMDBRating": imdb_rating, "movieNoIMDBVotes": no_imdb_vote, "movieGenres": movie_genres, "characterID1": char_id_1, "characterID2": char_id_2, "characterName1": char_name_1, "characterName2": char_name_2, "utterance": {"text": lines_texts, "LineID": line_ids}, }