The best datasets for training NLP models depend on the task, but several widely used options provide strong foundations. For general-purpose pretraining, large text corpora like Wikipedia, BookCorpus, and Common Crawl are popular. These datasets offer diverse, unstructured text that helps models learn grammar, context, and world knowledge. For example, BERT and GPT models were initially trained on Wikipedia and BookCorpus. Common Crawl’s C4 dataset (Colossal Clean Crawled Corpus) is a cleaned version of web text, often used for training models like T5. These datasets are valuable because they’re large (terabytes of data) and cover a broad range of topics, though they require significant preprocessing to filter noise.
For specific NLP tasks, task-focused datasets are essential. GLUE (General Language Understanding Evaluation) and SuperGLUE benchmarks provide collections of smaller datasets for tasks like sentiment analysis, textual entailment, and question answering. For example, the Stanford Sentiment Treebank (part of GLUE) offers fine-grained sentiment labels for movie reviews, while MultiNLI provides sentence pairs labeled for entailment. SQuAD (Stanford Question Answering Dataset) is a go-to for training QA models, with over 100,000 question-answer pairs based on Wikipedia articles. These datasets are smaller but carefully annotated, making them ideal for fine-tuning and evaluation.
Multilingual and domain-specific datasets address specialized needs. OSCAR is a multilingual corpus derived from Common Crawl, covering 166 languages, useful for training models like XLM-R. For translation, OPUS aggregates parallel texts (e.g., EU proceedings, movie subtitles) across 400+ languages. In specialized domains, BioBERT relies on PubMed abstracts for biomedical NLP, while CUAD (Contract Understanding Dataset) trains models to analyze legal contracts. For code-related tasks, CodeSearchNet provides annotated code snippets paired with natural language queries. Developers should prioritize datasets aligned with their use case, balancing size, quality, and domain relevance. Platforms like Hugging Face Datasets simplify access to many of these resources.