metadata
datasets:
- multi_nli
- snli
- scitail
language:
- en
metrics:
- accuracy
- f1
pipeline_tag: zero-shot-classification
RoBERTa NLI (Natural Language Inference)
This model is a fine-tuned model of roberta-large after being trained on a mixture of NLI datasets.
This model can classify a pair of sentence (a premise and a claim) into 3 classes:
- 'entailment': the claim can logically be inferred from the premise
- 'contradiction': the claim contradicts the premise
- 'neutral': the premise is unrelated or do not provide sufficient information to validate the claim
This model can also be used for zero-shot classification tasks ! Please take a look at this repo for more information on zero-shot classification tasks.
Usage
This model has been trained in an efficient way and thus cannot be load directly from HuggingFace's hub. To use that model, please follow instructions on this repo.
For zero-shot classification tasks, please take a look at this repo.
Data used for training
- multi_nli
- snli
- scitail
Evaluation results
| Data | Accuracy |
|---|---|
| MNLI (val. m) | 0.894 |
| MNLI (val. mm) | 0.895 |
| SNLI (val.) | 0.920 |
| SciTail (val.) | 0.934 |