03 Mar Top 10 Hugging Face datasets

Hugging Face hosts thousands of datasets on its Hub, covering a wide range of tasks, languages, and domains. Let us see the top 10 datasets. First, let us see why we should use the datasets from Hugging Face.

Why Use Hugging Face Datasets?

• Diversity: Covers a wide range of tasks, languages, and domains.
• Ease of Use: Simple API for loading and preprocessing datasets.
• Community Contributions: Thousands of datasets shared by the community.
• Integration: Works seamlessly with Hugging Face’s Transformers library.

The following is a list of 10 popular and widely used datasets on Hugging Face, along with their use cases:

IMDB Movie Reviews

• Description: A dataset of 50,000 movie reviews labeled as positive or negative for binary sentiment classification.
• Use Case: Sentiment analysis, text classification.
• Link: IMDB Dataset

SQuAD (Stanford Question Answering Dataset)

• Description: A reading comprehension dataset consisting of questions posed on a set of Wikipedia articles, where the answer to every question is a segment of text from the corresponding article.
• Use Case: Question answering, machine comprehension.
• Link: SQuAD Dataset

GLUE (General Language Understanding Evaluation)

• Description: A collection of datasets for evaluating natural language understanding systems. Includes tasks like sentiment analysis, textual entailment, and paraphrase detection.
• Use Case: Benchmarking NLP models, multi-task learning.
• Link: GLUE Dataset

Common Crawl

• Description: A massive dataset of web-crawled text data, available in multiple languages and domains.
• Use Case: Pretraining language models, multilingual NLP.
• Link: Common Crawl Dataset

COCO (Common Objects in Context)

• Description: A large-scale dataset for image captioning and object detection, containing images with captions and object annotations.
• Use Case: Image captioning, computer vision, multimodal tasks.
• Link: COCO Dataset

WikiText

• Description: A dataset of Wikipedia articles for language modeling, available in multiple versions (e.g., WikiText-2, WikiText-103).
• Use Case: Language modeling, text generation.
• Link: WikiText Dataset

XSum (Extreme Summarization)

• Description: A dataset for abstractive summarization, consisting of BBC articles and their one-sentence summaries.
• Use Case: Text summarization, sequence-to-sequence modeling.
• Link: XSum Dataset

MultiNLI (Multi-Genre Natural Language Inference)

• Description: A dataset for natural language inference, where the goal is to determine whether a hypothesis is true, false, or neutral given a premise.
• Use Case: Textual entailment, sentence pair classification.
• Link: MultiNLI Dataset

CoNLL-2003

• Description: A dataset for named entity recognition (NER), containing text annotated with four entity types: PER, LOC, ORG, and MISC.
• Use Case: Named entity recognition, sequence labeling.
• Link: CoNLL-2003 Dataset

OpenWebText

• Description: A large-scale dataset of web text, often used for training large language models.
• Use Case: Pretraining language models, text generation.
• Link: OpenWebText Dataset

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