Update files from the datasets library (from 1.0.0)

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

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

cornell_movie_dialog.py

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+"""TODO(cornell_movie_dialog): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+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},
+            }

dataset_infos.json

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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

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+size 1584