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Update files from the datasets library (from 1.0.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bin.* filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.xz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zstandard filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
cornell_movie_dialog.py ADDED
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+ """TODO(cornell_movie_dialog): Add a description here."""
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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import ast
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+ import os
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+
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+ import datasets
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+
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+
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+ # TODO(cornell_movie_dialog): BibTeX citation
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+ _CITATION = """\
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+ @InProceedings{Danescu-Niculescu-Mizil+Lee:11a,
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+
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+ author={Cristian Danescu-Niculescu-Mizil and Lillian Lee},
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+
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+ title={Chameleons in imagined conversations:
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+ A new approach to understanding coordination of linguistic style in dialogs.},
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+
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+ booktitle={Proceedings of the
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+
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+ Workshop on Cognitive Modeling and Computational Linguistics, ACL 2011},
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+
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+ year={2011}
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+
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+ }
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+ """
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+
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+ # TODO(cornell_movie_dialog):
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+ _DESCRIPTION = """\
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+ This corpus contains a large metadata-rich collection of fictional conversations extracted from raw movie scripts:
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+ - 220,579 conversational exchanges between 10,292 pairs of movie characters
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+ - involves 9,035 characters from 617 movies
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+ - in total 304,713 utterances
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+ - movie metadata included:
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+ - genres
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+ - release year
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+ - IMDB rating
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+ - number of IMDB votes
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+ - IMDB rating
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+ - character metadata included:
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+ - gender (for 3,774 characters)
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+ - position on movie credits (3,321 characters)
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+ """
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+
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+ _URL = "https://www.cs.cornell.edu/~cristian/data/cornell_movie_dialogs_corpus.zip"
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+
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+
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+ class CornellMovieDialog(datasets.GeneratorBasedBuilder):
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+ """TODO(cornell_movie_dialog): Short description of my dataset."""
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+
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+ # TODO(cornell_movie_dialog): Set up version.
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+ VERSION = datasets.Version("0.1.0")
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+
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+ def _info(self):
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+ # TODO(cornell_movie_dialog): Specifies the datasets.DatasetInfo object
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+ return datasets.DatasetInfo(
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+ # This is the description that will appear on the datasets page.
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+ description=_DESCRIPTION,
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+ # datasets.features.FeatureConnectors
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+ features=datasets.Features(
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+ {
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+ "movieID": datasets.Value("string"),
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+ "movieTitle": datasets.Value("string"),
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+ "movieYear": datasets.Value("string"),
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+ "movieIMDBRating": datasets.Value("string"),
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+ "movieNoIMDBVotes": datasets.Value("string"),
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+ "movieGenres": datasets.features.Sequence(datasets.Value("string")),
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+ "characterID1": datasets.Value("string"),
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+ "characterID2": datasets.Value("string"),
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+ "characterName1": datasets.Value("string"),
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+ "characterName2": datasets.Value("string"),
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+ "utterance": datasets.features.Sequence(
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+ {"text": datasets.Value("string"), "LineID": datasets.Value("string")}
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+ )
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+ # These are the features of your dataset like images, labels ...
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+ }
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+ ),
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+ # If there's a common (input, target) tuple from the features,
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+ # specify them here. They'll be used if as_supervised=True in
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+ # builder.as_dataset.
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+ supervised_keys=None,
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+ # Homepage of the dataset for documentation
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+ homepage="http://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html",
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ """Returns SplitGenerators."""
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+ # TODO(cornell_movie_dialog): Downloads the data and defines the splits
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+ # dl_manager is a datasets.download.DownloadManager that can be used to
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+ # download and extract URLs
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+ dl_dir = dl_manager.download_and_extract(_URL)
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={"filepaths": os.path.join(dl_dir, "cornell movie-dialogs corpus")},
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+ ),
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+ ]
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+
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+ def _generate_examples(self, filepaths):
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+ """Yields examples."""
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+ # TODO(cornell_movie_dialog): Yields (key, example) tuples from the dataset
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+ movie_char_file = os.path.join(filepaths, "movie_characters_metadata.txt")
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+ movie_conv_file = os.path.join(filepaths, "movie_conversations.txt")
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+ movie_lines_file = os.path.join(filepaths, "movie_lines.txt")
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+ movie_titles_file = os.path.join(filepaths, "movie_titles_metadata.txt")
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+
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+ with open(movie_char_file, "rb") as f:
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+ movie_char_data = [x.decode("latin").split("+++$+++") for x in f.readlines()]
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+
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+ with open(movie_conv_file, "rb") as f:
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+ movie_conv_data = [x.decode("latin").split("+++$+++") for x in f.readlines()]
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+
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+ with open(movie_lines_file, "rb") as f:
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+ movie_lines_data = [x.decode("latin").split("+++$+++") for x in f.readlines()]
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+
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+ with open(movie_titles_file, "rb") as f:
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+ movie_titles_data = [x.decode("latin").split("+++$+++") for x in f.readlines()]
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+ # looping over movie conversation file
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+ for id_, conv in enumerate(movie_conv_data):
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+ char_id_1 = conv[0]
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+ char_id_2 = conv[1]
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+ movie_id = conv[2]
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+ line_ids = conv[-1].replace("\n", "")
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+ line_ids = ast.literal_eval(line_ids.strip())
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+ lines_texts = []
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+ # searching text corresponding to each lineID in line_ids in movie lines file
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+ for line_id in line_ids:
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+ i = 0
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+ while i < len(movie_lines_data) and movie_lines_data[i][0].strip() != line_id:
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+ i += 1
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+ lines_texts.append(movie_lines_data[i][0]) # if i < len(movie_lines_data) else '')
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+ # look for char names in movie character file
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+ j = 0
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+ while j < len(movie_char_data) and movie_char_data[j][0].strip() != char_id_1.strip():
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+ j += 1
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+ char_name_1 = movie_char_data[j][1] # if j < len(movie_char_data) else ''
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+ movie_title = movie_char_data[j][3] # if j < len(movie_char_data) else ''
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+
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+ k = 0
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+ while k < len(movie_char_data) and movie_char_data[k][0].strip() != char_id_2.strip():
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+ k += 1
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+ char_name_2 = movie_char_data[k][1]
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+
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+ # look for movie year, IMDBRating, genre, no_imdb_voting in movie tiles file
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+ li = 0
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+ while li < len(movie_titles_data) and movie_titles_data[li][0].strip() != movie_id.strip():
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+ li += 1
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+ movie_year = movie_titles_data[li][2]
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+ imdb_rating = movie_titles_data[li][3]
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+ no_imdb_vote = movie_titles_data[li][4]
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+ genre = movie_titles_data[li][5].replace("\n", "").strip()
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+ movie_genres = ast.literal_eval(genre)
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+
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+ yield id_, {
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+ "movieID": movie_id,
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+ "movieTitle": movie_title,
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+ "movieYear": movie_year,
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+ "movieIMDBRating": imdb_rating,
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+ "movieNoIMDBVotes": no_imdb_vote,
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+ "movieGenres": movie_genres,
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+ "characterID1": char_id_1,
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+ "characterID2": char_id_2,
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+ "characterName1": char_name_1,
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+ "characterName2": char_name_2,
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+ "utterance": {"text": lines_texts, "LineID": line_ids},
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+ }
dataset_infos.json ADDED
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+ {"default": {"description": " \nThis corpus contains a large metadata-rich collection of fictional conversations extracted from raw movie scripts:\n- 220,579 conversational exchanges between 10,292 pairs of movie characters\n- involves 9,035 characters from 617 movies\n- in total 304,713 utterances\n- movie metadata included:\n - genres\n - release year\n - IMDB rating\n - number of IMDB votes\n - IMDB rating\n- character metadata included:\n - gender (for 3,774 characters)\n - position on movie credits (3,321 characters)\n", "citation": " @InProceedings{Danescu-Niculescu-Mizil+Lee:11a,\n\n author={Cristian Danescu-Niculescu-Mizil and Lillian Lee},\n\n title={Chameleons in imagined conversations: \n A new approach to understanding coordination of linguistic style in dialogs.},\n\n booktitle={Proceedings of the \n\n Workshop on Cognitive Modeling and Computational Linguistics, ACL 2011},\n\n year={2011}\n\n}\n", "homepage": "http://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html", "license": "", "features": {"movieID": {"dtype": "string", "id": null, "_type": "Value"}, "movieTitle": {"dtype": "string", "id": null, "_type": "Value"}, "movieYear": {"dtype": "string", "id": null, "_type": "Value"}, "movieIMDBRating": {"dtype": "string", "id": null, "_type": "Value"}, "movieNoIMDBVotes": {"dtype": "string", "id": null, "_type": "Value"}, "movieGenres": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "characterID1": {"dtype": "string", "id": null, "_type": "Value"}, "characterID2": {"dtype": "string", "id": null, "_type": "Value"}, "characterName1": {"dtype": "string", "id": null, "_type": "Value"}, "characterName2": {"dtype": "string", "id": null, "_type": "Value"}, "utterance": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "LineID": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "cornell_movie_dialog", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 19548840, "num_examples": 83097, "dataset_name": "cornell_movie_dialog"}}, "download_checksums": {"https://www.cs.cornell.edu/~cristian/data/cornell_movie_dialogs_corpus.zip": {"num_bytes": 9916637, "checksum": "3bde8a571f615201bc2d2453e22878090719638592f774720eddec739de8c900"}}, "download_size": 9916637, "dataset_size": 19548840, "size_in_bytes": 29465477}}
dummy/0.1.0/dummy_data.zip ADDED
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+ size 1584