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README.md

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----
-pretty_name: IMDB
-annotations_creators:
-- expert-generated
-language_creators:
-- expert-generated
-language:
-- en
-license:
-- other
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- text-classification
-task_ids:
-- sentiment-classification
-paperswithcode_id: imdb-movie-reviews
-train-eval-index:
-- config: plain_text
-  task: text-classification
-  task_id: binary_classification
-  splits:
-    train_split: train
-    eval_split: test
-  col_mapping:
-    text: text
-    label: target
-  metrics:
-  - type: accuracy
-  - name: Accuracy
-  - type: f1
-    name: F1 macro
-    args:
-      average: macro
-  - type: f1
-    name: F1 micro
-    args:
-      average: micro
-  - type: f1
-    name: F1 weighted
-    args:
-      average: weighted
-  - type: precision
-    name: Precision macro
-    args:
-      average: macro
-  - type: precision
-    name: Precision micro
-    args:
-      average: micro
-  - type: precision
-    name: Precision weighted
-    args:
-      average: weighted
-  - type: recall
-    name: Recall macro
-    args:
-      average: macro
-  - type: recall
-    name: Recall micro
-    args:
-      average: micro
-  - type: recall
-    name: Recall weighted
-    args:
-      average: weighted
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: neg
-          1: pos
-  config_name: plain_text
-  splits:
-  - name: train
-    num_bytes: 33432835
-    num_examples: 25000
-  - name: test
-    num_bytes: 32650697
-    num_examples: 25000
-  - name: unsupervised
-    num_bytes: 67106814
-    num_examples: 50000
-  download_size: 84125825
-  dataset_size: 133190346
----
-
-# Dataset Card for "imdb"
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [http://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/)
-- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Size of downloaded dataset files:** 80.23 MB
-- **Size of the generated dataset:** 127.06 MB
-- **Total amount of disk used:** 207.28 MB
-
-### Dataset Summary
-
-Large Movie Review Dataset.
-This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Dataset Structure
-
-### Data Instances
-
-#### plain_text
-
-- **Size of downloaded dataset files:** 80.23 MB
-- **Size of the generated dataset:** 127.06 MB
-- **Total amount of disk used:** 207.28 MB
-
-An example of 'train' looks as follows.
-```
-{
-    "label": 0,
-    "text": "Goodbye world2\n"
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### plain_text
-- `text`: a `string` feature.
-- `label`: a classification label, with possible values including `neg` (0), `pos` (1).
-
-### Data Splits
-
-|   name   |train|unsupervised|test |
-|----------|----:|-----------:|----:|
-|plain_text|25000|       50000|25000|
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Citation Information
-
-```
-@InProceedings{maas-EtAl:2011:ACL-HLT2011,
-  author    = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},
-  title     = {Learning Word Vectors for Sentiment Analysis},
-  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
-  month     = {June},
-  year      = {2011},
-  address   = {Portland, Oregon, USA},
-  publisher = {Association for Computational Linguistics},
-  pages     = {142--150},
-  url       = {http://www.aclweb.org/anthology/P11-1015}
-}
-
-```
-
-
-### Contributions
-
-Thanks to [@ghazi-f](https://github.com/ghazi-f), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

dataset_infos.json

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-{"plain_text": {"description": "Large Movie Review Dataset.\nThis is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.", "citation": "@InProceedings{maas-EtAl:2011:ACL-HLT2011,\n  author    = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},\n  title     = {Learning Word Vectors for Sentiment Analysis},\n  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},\n  month     = {June},\n  year      = {2011},\n  address   = {Portland, Oregon, USA},\n  publisher = {Association for Computational Linguistics},\n  pages     = {142--150},\n  url       = {http://www.aclweb.org/anthology/P11-1015}\n}\n", "homepage": "http://ai.stanford.edu/~amaas/data/sentiment/", "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": null, "task_templates": [{"task": "text-classification", "text_column": "text", "label_column": "label", "labels": ["neg", "pos"]}], "builder_name": "imdb", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 33432835, "num_examples": 25000, "dataset_name": "imdb"}, "test": {"name": "test", "num_bytes": 32650697, "num_examples": 25000, "dataset_name": "imdb"}, "unsupervised": {"name": "unsupervised", "num_bytes": 67106814, "num_examples": 50000, "dataset_name": "imdb"}}, "download_checksums": {"http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz": {"num_bytes": 84125825, "checksum": "c40f74a18d3b61f90feba1e17730e0d38e8b97c05fde7008942e91923d1658fe"}}, "download_size": 84125825, "post_processing_size": null, "dataset_size": 133190346, "size_in_bytes": 217316171}}

imdb.py

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-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""IMDB movie reviews dataset."""
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DESCRIPTION = """\
-Large Movie Review Dataset.
-This is a dataset for binary sentiment classification containing substantially \
-more data than previous benchmark datasets. We provide a set of 25,000 highly \
-polar movie reviews for training, and 25,000 for testing. There is additional \
-unlabeled data for use as well.\
-"""
-
-_CITATION = """\
-@InProceedings{maas-EtAl:2011:ACL-HLT2011,
-  author    = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},
-  title     = {Learning Word Vectors for Sentiment Analysis},
-  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
-  month     = {June},
-  year      = {2011},
-  address   = {Portland, Oregon, USA},
-  publisher = {Association for Computational Linguistics},
-  pages     = {142--150},
-  url       = {http://www.aclweb.org/anthology/P11-1015}
-}
-"""
-
-_DOWNLOAD_URL = "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"
-
-
-class IMDBReviewsConfig(datasets.BuilderConfig):
-    """BuilderConfig for IMDBReviews."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for IMDBReviews.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(IMDBReviewsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-
-
-class Imdb(datasets.GeneratorBasedBuilder):
-    """IMDB movie reviews dataset."""
-
-    BUILDER_CONFIGS = [
-        IMDBReviewsConfig(
-            name="plain_text",
-            description="Plain text",
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["neg", "pos"])}
-            ),
-            supervised_keys=None,
-            homepage="http://ai.stanford.edu/~amaas/data/sentiment/",
-            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={"files": dl_manager.iter_archive(archive), "split": "train"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "test"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split("unsupervised"),
-                gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train", "labeled": False},
-            ),
-        ]
-
-    def _generate_examples(self, files, split, labeled=True):
-        """Generate aclImdb examples."""
-        # For labeled examples, extract the label from the path.
-        if labeled:
-            label_mapping = {"pos": 1, "neg": 0}
-            for path, f in files:
-                if path.startswith(f"aclImdb/{split}"):
-                    label = label_mapping.get(path.split("/")[2])
-                    if label is not None:
-                        yield path, {"text": f.read().decode("utf-8"), "label": label}
-        else:
-            for path, f in files:
-                if path.startswith(f"aclImdb/{split}"):
-                    if path.split("/")[2] == "unsup":
-                        yield path, {"text": f.read().decode("utf-8"), "label": -1}

plain_text/imdb-test.parquet

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