SNLI
The SNLI dataset offers 570,000 pairs of manually annotated English sentences for the classification into nicking, contradiction, or neutral, essential for textual inference in NLP.
Description
SNLI is a major corpus for the Natural Language Inference (NLI) task. Each example contains a “premise” sentence and a “hypothesis” sentence annotated with a label indicating whether the second one implies the first one, contradicts it, or is neutral.
What is this dataset for?
- Train and evaluate textual inference models
- Improving semantic understanding in NLP systems
- Test the ability of models to reason about logical relationships between sentences
Can it be enriched or improved?
Yes, by adding examples in other languages, by enriching the annotations, or by integrating explanations of decisions for more transparent learning.
🔎 In summary
🧠 Recommended for
- NLP researchers
- AI developers
- Deep learning students
🔧 Compatible tools
- Hugging Face Transformers
- PyTorch
- TensorFlow
- AllennLP
💡 Tip
Use SNLI in conjunction with other NLI datasets to improve model robustness.
Frequently Asked Questions
What type of annotations does the SNLI dataset contain?
Manual labels indicating whether the second sentence implies, contradicts, or is neutral to the first sentence.
Is the dataset suitable for commercial use?
Yes, the CC-BY-SA 4.0 license allows commercial use under the condition of sharing the same.
What is the average sentence size in SNLI?
On average, the “premise” sentence contains 14.1 tokens and the “hypothesis” 8.3 tokens.
