Cross Lingual Natural Language Inference On 3
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | Paper Title | Repository |
|---|---|---|---|
| XLM-R R4F | 84.2% | Better Fine-Tuning by Reducing Representational Collapse | |
| BERT | 70.5% | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | |
| X-BiLSTM | 67.7% | Supervised Learning of Universal Sentence Representations from Natural Language Inference Data | |
| X-CBOW | 61.0% | Supervised Learning of Universal Sentence Representations from Natural Language Inference Data |
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