The best datasets for training natural language processing (NLP) models depend on the task and the type of model being built. For general-purpose pretraining of language models like BERT or GPT, large text corpora such as Wikipedia, Common Crawl, and BooksCorpus are widely used. These datasets provide diverse, unstructured text that helps models learn grammar, context, and factual knowledge. For example, the original BERT model was trained on BooksCorpus (800M words) and English Wikipedia (2.5B words). However, Common Crawl (containing petabytes of web data) requires careful filtering to remove low-quality or duplicate content. For task-specific training, datasets like GLUE, SuperGLUE, and SQuAD are standard benchmarks. These include labeled data for tasks like sentiment analysis, question answering, and text classification, making them ideal for fine-tuning models.
Task-specific datasets are critical for evaluating and refining model performance. GLUE (General Language Understanding Evaluation) and its successor SuperGLUE bundle multiple tasks—such as textual entailment (MNLI), sentiment analysis (SST-2), and paraphrase detection (QQP)—into a single benchmark. These are often used to test a model’s generalizability. For question answering, SQuAD (Stanford Question Answering Dataset) provides 100,000+ question-answer pairs based on Wikipedia articles. Named entity recognition (NER) models often rely on CoNLL-2003, which labels entities like people, locations, and organizations in news text. For dialogue systems, Cornell Movie Dialogs or MultiWOZ offer structured conversational data. When selecting a dataset, consider its size, label quality, and alignment with your target application—for example, legal or medical NLP might require domain-specific data like CaseLaw or MIMIC-III.
Domain-specific or multilingual use cases demand specialized datasets. Biomedical NLP models often use PubMed abstracts or MIMIC-III, which includes de-identified medical records. Legal NLP might leverage CaseLaw Access Project data or EUR-Lex for EU legal documents. For multilingual models, OPUS (a collection of translated texts) and XTREME (covering 40+ languages) provide cross-lingual benchmarks. Low-resource languages can benefit from datasets like FLORES-101 for machine translation. Always verify licensing and ethical considerations—for instance, Common Crawl’s web data may contain biased or sensitive content. Tools like Hugging Face’s datasets library simplify access to many of these datasets, offering preprocessed versions with standardized splits. Prioritize datasets with clear documentation, reproducibility, and community adoption to streamline development.