Cross Lingual Natural Language Inference On
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Paper Title | Repository |
|---|---|---|---|
| XLM-R R4F | 84.7% | Better Fine-Tuning by Reducing Representational Collapse | |
| X-BiLSTM | 67.7% | Supervised Learning of Universal Sentence Representations from Natural Language Inference Data | |
| X-CBOW | 60.3% | Supervised Learning of Universal Sentence Representations from Natural Language Inference Data |
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