Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
License:
Update files from the datasets library (from 1.8.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.8.0
- README.md +2 -0
- dataset_infos.json +1 -1
- rotten_tomatoes.py +2 -0
README.md
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---
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paperswithcode_id: null
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---
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languages:
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- en
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paperswithcode_id: null
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---
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dataset_infos.json
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{"default": {"description": "Movie Review Dataset.\nThis is a dataset of containing 5,331 positive and 5,331 negative processed
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{"default": {"description": "Movie Review Dataset.\nThis is a dataset of containing 5,331 positive and 5,331 negative processed\nsentences from Rotten Tomatoes movie reviews. This data was first used in Bo\nPang and Lillian Lee, ``Seeing stars: Exploiting class relationships for\nsentiment categorization with respect to rating scales.'', Proceedings of the\nACL, 2005.\n", "citation": "@InProceedings{Pang+Lee:05a,\n author = {Bo Pang and Lillian Lee},\n title = {Seeing stars: Exploiting class relationships for sentiment\n categorization with respect to rating scales},\n booktitle = {Proceedings of the ACL},\n year = 2005\n}\n", "homepage": "http://www.cs.cornell.edu/people/pabo/movie-review-data/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["neg", "pos"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "", "output": ""}, "task_templates": [{"task": "text-classification", "text_column": "text", "label_column": "label", "labels": ["neg", "pos"]}], "builder_name": "rotten_tomatoes_movie_review", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1074810, "num_examples": 8530, "dataset_name": "rotten_tomatoes_movie_review"}, "validation": {"name": "validation", "num_bytes": 134679, "num_examples": 1066, "dataset_name": "rotten_tomatoes_movie_review"}, "test": {"name": "test", "num_bytes": 135972, "num_examples": 1066, "dataset_name": "rotten_tomatoes_movie_review"}}, "download_checksums": {"https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz": {"num_bytes": 487770, "checksum": "a05befe52aafda71d458d188a1c54506a998b1308613ba76bbda2e5029409ce9"}}, "download_size": 487770, "post_processing_size": null, "dataset_size": 1345461, "size_in_bytes": 1833231}}
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rotten_tomatoes.py
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import os
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import datasets
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_DESCRIPTION = """\
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supervised_keys=[""],
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homepage="http://www.cs.cornell.edu/people/pabo/movie-review-data/",
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citation=_CITATION,
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)
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def _vocab_text_gen(self, train_file):
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import os
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import datasets
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from datasets.tasks import TextClassification
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_DESCRIPTION = """\
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supervised_keys=[""],
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homepage="http://www.cs.cornell.edu/people/pabo/movie-review-data/",
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citation=_CITATION,
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task_templates=[TextClassification(text_column="text", label_column="label")],
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)
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def _vocab_text_gen(self, train_file):
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