| import datasets | |
| from datasets.tasks import TextClassification | |
| _DESCRIPTION = """ | |
| Movie Review Dataset. | |
| This is a dataset containing 4,265 positive and 4,265 negative processed | |
| sentences from Rotten Tomatoes movie reviews. | |
| """ | |
| _CITATION = """ | |
| @InProceedings{Pang+Lee:05a, | |
| author = {Bo Pang and Lillian Lee}, | |
| title = {Seeing stars: Exploiting class relationships for sentiment | |
| categorization with respect to rating scales}, | |
| booktitle = {Proceedings of the ACL}, | |
| year = 2005 | |
| } | |
| """ | |
| _DOWNLOAD_URL = "https://testerstories.com/files/ai_learn/rt-polaritydata.tar.gz" | |
| class RottenTomatoesReviews(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.0.0") | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "text": datasets.Value("string"), | |
| "label": datasets.features.ClassLabel(names=["neg", "pos"]), | |
| } | |
| ), | |
| supervised_keys=[""], | |
| homepage="http://www.cs.cornell.edu/people/pabo/movie-review-data/", | |
| citation=_CITATION, | |
| task_templates=[ | |
| TextClassification(text_column="text", label_column="label") | |
| ], | |
| ) | |
| def _split_generators(self, dl_manager): | |
| archive = dl_manager.download(_DOWNLOAD_URL) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "split_key": "train", | |
| "files": dl_manager.iter_archive(archive), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "split_key": "validation", | |
| "files": dl_manager.iter_archive(archive), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "split_key": "test", | |
| "files": dl_manager.iter_archive(archive), | |
| }, | |
| ), | |
| ] | |
| def _get_examples_from_split(self, split_key, files): | |
| data_dir = "rt-polaritydata/" | |
| pos_samples, neg_samples = None, None | |
| for path, f in files: | |
| if path == data_dir + "rt-polarity.pos": | |
| pos_samples = [line.decode("latin-1").strip() for line in f] | |
| elif path == data_dir + "rt-polarity.neg": | |
| neg_samples = [line.decode("latin-1").strip() for line in f] | |
| if pos_samples is not None and neg_samples is not None: | |
| break | |
| i1 = int(len(pos_samples) * 0.8 + 0.5) | |
| i2 = int(len(pos_samples) * 0.9 + 0.5) | |
| train_samples = pos_samples[:i1] + neg_samples[:i1] | |
| train_labels = (["pos"] * i1) + (["neg"] * i1) | |
| validation_samples = pos_samples[i1:i2] + neg_samples[i1:i2] | |
| validation_labels = (["pos"] * (i2 - i1)) + (["neg"] * (i2 - i1)) | |
| test_samples = pos_samples[i2:] + neg_samples[i2:] | |
| test_labels = (["pos"] * (len(pos_samples) - i2)) + ( | |
| ["neg"] * (len(pos_samples) - i2) | |
| ) | |
| if split_key == "train": | |
| return (train_samples, train_labels) | |
| if split_key == "validation": | |
| return (validation_samples, validation_labels) | |
| if split_key == "test": | |
| return (test_samples, test_labels) | |
| else: | |
| raise ValueError(f"Invalid split key {split_key}") | |
| def _generate_examples(self, split_key, files): | |
| split_text, split_labels = self._get_examples_from_split(split_key, files) | |
| for text, label in zip(split_text, split_labels): | |
| data_key = split_key + "_" + text | |
| feature_dict = {"text": text, "label": label} | |
| yield data_key, feature_dict | |