From the model card
Fields below are copied from the tags and counters on the HuggingFace repository FacebookAI/roberta-large-mnli at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- FacebookAI
- Pipeline tag
- text-classification
- Library
- Transformers
- Framework tags
- PyTorch, TensorFlow, JAX
- Weight formats
- safetensors
- License tag
mit— read the license file in the repo before relying on it- Language tags
- English (en)
- Papers cited
- arXiv:1907.11692, arXiv:1806.02847, arXiv:1804.07461, arXiv:1704.05426, arXiv:1508.05326, arXiv:1809.05053, arXiv:1910.09700
- Datasets declared
- multi_nli, wikipedia, bookcorpus
- Downloads (HF counter at last fetch)
- 349,227
- Likes (HF counter at last fetch)
- 210
- Model card
- https://huggingface.co/FacebookAI/roberta-large-mnli
Use cases
- Zero-shot text classification via hypothesis entailment scoring
- Document intent or topic classification without labeled data
- Textual entailment for fact-checking pipeline components
- Transfer learning baseline for NLI benchmark tasks
Pros
- RoBERTa-large provides strong NLI quality for zero-shot classification
- Well-established baseline with extensive literature comparison
- Easy integration via pipeline('zero-shot-classification') in transformers
- MIT license
Cons
- DeBERTa-v3-large-mnli outperforms it on most zero-shot benchmarks
- Zero-shot via NLI is slower and less accurate than a trained classifier when labels are available
- Large model size (355M) for an encoder — slower than BERT-base alternatives
- Multi-genre NLI training may not generalize well to domain-specific text
Tags
transformerspytorchtfjaxsafetensorsrobertatext-classificationautogenerated-modelcardendataset:multi_nlidataset:wikipediadataset:bookcorpusarxiv:1907.11692arxiv:1806.02847arxiv:1804.07461arxiv:1704.05426arxiv:1508.05326arxiv:1809.05053arxiv:1910.09700license:mit