Natural Language Inference On Terra
Metrics
Accuracy
Results
Performance results of various models on this benchmark
| Paper Title | ||
|---|---|---|
| Human Benchmark | 0.92 | RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark |
| Golden Transformer | 0.871 | - |
| ruRoberta-large finetune | 0.801 | - |
| ruT5-large-finetune | 0.747 | - |
| ruBert-large finetune | 0.704 | - |
| ruBert-base finetune | 0.703 | - |
| ruT5-base-finetune | 0.692 | - |
| RuGPT3Large | 0.654 | - |
| RuBERT plain | 0.642 | - |
| RuBERT conversational | 0.64 | - |
| SBERT_Large_mt_ru_finetuning | 0.637 | - |
| SBERT_Large | 0.637 | - |
| Multilingual Bert | 0.617 | - |
| YaLM 1.0B few-shot | 0.605 | - |
| RuGPT3XL few-shot | 0.573 | - |
| MT5 Large | 0.561 | mT5: A massively multilingual pre-trained text-to-text transformer |
| heuristic majority | 0.549 | Unreasonable Effectiveness of Rule-Based Heuristics in Solving Russian SuperGLUE Tasks |
| majority_class | 0.513 | Unreasonable Effectiveness of Rule-Based Heuristics in Solving Russian SuperGLUE Tasks |
| RuGPT3Medium | 0.505 | - |
| RuGPT3Small | 0.488 | - |
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