# What are the best datasets for training NLP models?

Source: https://zilliz.com/ai-faq/what-are-the-best-datasets-for-training-nlp-models
Author: zilliz

The best datasets for training NLP models depend on the specific task and domain. For general language understanding, large corpora like Common Crawl, Wikipedia, and BookCorpus provide a foundation for pre-training models. Specific NLP tasks require tailored datasets: 

 - Text Classification: Datasets like IMDb, AG News, and Yelp Reviews are commonly used for tasks like sentiment analysis or topic classification. 
 - Machine Translation: Benchmarks like WMT (e.g., Europarl and ParaCrawl) and IWSLT are gold standards for translation tasks. 
 - Question Answering: Datasets such as SQuAD, TriviaQA, and Natural Questions provide well-annotated examples for training models to retrieve accurate answers. 
 - Named Entity Recognition (NER): CoNLL-2003 and OntoNotes are widely used for identifying entities in text. 

 For benchmarking NLP models, datasets like GLUE, SuperGLUE, and XNLI evaluate performance across multiple tasks and languages. Low-resource language tasks benefit from datasets like FLORES or multilingual Common Crawl. Hugging Face’s Datasets library consolidates many of these datasets into a single repository, simplifying access and experimentation. Selecting the right dataset is crucial, as it impacts the quality and relevance of the trained model. Developers often augment datasets with domain-specific text or synthetically generated examples to address niche requirements.