imdb

# 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:** 84.13 MB
- **Size of the generated dataset:** 133.23 MB
- **Total amount of disk used:** 217.35 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:** 84.13 MB
- **Size of the generated dataset:** 133.23 MB
- **Total amount of disk used:** 217.35 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.

<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">IMDB分类（ImdbClassification）</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">属于<a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">大规模文本嵌入基准（MTEB）</a>数据集</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">大规模文本嵌入基准（Massive Text Embedding Benchmark）</div>
</div>

<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->

大型电影评论数据集

| 任务类别 | t2c |
|---------|-----|
| 领域 | 评论类、书面文本 |
| 参考文献 | http://www.aclweb.org/anthology/P11-1015 |

## 本任务评估指南

您可以通过如下代码在本数据集上评估嵌入模型：

python
import mteb

task = mteb.get_tasks(["ImdbClassification"])
evaluator = mteb.MTEB(task)

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
若需了解如何在MTEB任务中运行模型，请访问[GitHub仓库](https://github.com/embeddings-benchmark/mteb)。

## 引用规范

若您使用本数据集，请同时引用该数据集与[MTEB](https://github.com/embeddings-benchmark/mteb)，因为本数据集的额外处理工作属于[MMTEB贡献项目](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb)的一部分。

bibtex

@inproceedings{maas-etal-2011-learning,
address = {Portland, Oregon, USA},
author = {Maas, Andrew L. and
Daly, Raymond E. and
Pham, Peter T. and
Huang, Dan and
Ng, Andrew Y. and
Potts, Christopher},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
editor = {Lin, Dekang and
Matsumoto, Yuji and
Mihalcea, Rada},
month = jun,
pages = {142--150},
publisher = {Association for Computational Linguistics},
title = {Learning Word Vectors for Sentiment Analysis},
url = {https://aclanthology.org/P11-1015},
year = {2011},
}

@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Lo{"i}c Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{"i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}

# 数据集统计信息
<details>
<summary> 数据集统计信息</summary>

以下代码展示了本任务的描述性统计数据，您也可以通过如下代码获取：

python
import mteb

task = mteb.get_task("ImdbClassification")

desc_stats = task.metadata.descriptive_stats

json
{
"test": {
"num_samples": 25000,
"number_of_characters": 32344810,
"number_texts_intersect_with_train": 123,
"min_text_length": 32,
"average_text_length": 1293.7924,
"max_text_length": 12988,
"unique_text": 24801,
"unique_labels": 2,
"labels": {
"0": {
"count": 12500
},
"1": {
"count": 12500
}
}
},
"train": {
"num_samples": 25000,
"number_of_characters": 33126741,
"number_texts_intersect_with_train": null,
"min_text_length": 52,
"average_text_length": 1325.06964,
"max_text_length": 13704,
"unique_text": 24904,
"unique_labels": 2,
"labels": {
"0": {
"count": 12500
},
"1": {
"count": 12500
}
}
}
}

</details>

---
*本数据集卡片由[MTEB](https://github.com/embeddings-benchmark/mteb)自动生成*