Help me find a dataset for NLP fine-tuning with annotations and clear usage rights

Here are some solid options for NLP fine-tuning datasets with annotations and clear usage rights, depending on the task you want to fine-tune for.

Good general-purpose choices

1. GLUE / SuperGLUE

• Use case: Sentiment, entailment, paraphrase, QA-related classification
• Annotations: Yes
• License/rights: Public benchmark datasets with clearly documented usage through their source components
• Why it’s useful: Standard NLP fine-tuning benchmarks; widely supported

2. SQuAD v1.1 / v2.0

• Use case: Extractive question answering
• Annotations: Yes, spans and answerability labels
• License/rights: Openly available under the Stanford SQuAD terms; widely used for research and model training
• Why it’s useful: Clean annotation format and very common for QA fine-tuning

3. MultiNLI

• Use case: Natural language inference
• Annotations: Yes
• License/rights: Publicly available for research use; source data from multiple corpora, so check component terms if you need commercial use
• Why it’s useful: Strong baseline dataset for entailment tasks

4. CoNLL-2003

• Use case: Named entity recognition
• Annotations: Yes, token-level labels
• License/rights: Widely distributed for research; verify redistribution/commercial terms depending on source
• Why it’s useful: Classic NER dataset with clean annotation

Strong open-license datasets

5. GoEmotions

• Use case: Emotion classification
• Annotations: Yes
• License/rights: Released by Google with dataset documentation; check the official dataset card for exact terms
• Why it’s useful: High-quality multi-label emotion annotations

6. BoolQ

• Use case: Yes/no question answering
• Annotations: Yes
• License/rights: Part of SuperGLUE; see original source/license details
• Why it’s useful: Simple binary QA fine-tuning

7. TweetEval

• Use case: Sentiment, emotion, stance, irony, etc.
• Annotations: Yes
• License/rights: Typically distributed via benchmark papers and Hugging Face with clear dataset cards; check source-specific terms
• Why it’s useful: Good for social media NLP

If you need very clear commercial-friendly licensing

Look for datasets with:

• CC BY 4.0
• CC BY-SA
• Apache 2.0
• MIT
• Public domain / CC0

Good places to search:

• Hugging Face Datasets: filter by license
• Kaggle: always verify dataset-specific license
• OpenSLR for speech-related NLP
• Zenodo / Figshare for academic datasets with explicit licenses

Recommended search strategy

If you want a dataset that is:

• annotated
• suitable for fine-tuning
• has clear usage rights

then on Hugging Face Datasets, filter by:

1. Task: classification / NER / QA / summarization
2. License: CC BY, Apache 2.0, MIT, CC0
3. Size: small / medium / large depending on compute

Best default picks by task

• Sentiment: SST-2, GoEmotions
• NER: CoNLL-2003, WikiAnn
• QA: SQuAD v1.1/v2.0, BoolQ
• NLI: MultiNLI, SNLI
• Summarization: CNN/DailyMail, XSum
• Intent classification: Banking77, CLINC150

Important note

Even when a dataset is “available,” the original source license matters. For commercial use, avoid assuming a benchmark is safe unless the dataset card or original license explicitly allows it.

If you want, I can also give you:

1. a shortlist of datasets with permissive licenses only, or
2. datasets tailored to a specific task like sentiment, NER, QA, or summarization.