Common Sense Reasoning On Rwsd
評価指標
Accuracy
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
モデル名 | Accuracy | Paper Title | Repository |
---|---|---|---|
RuBERT conversational | 0.669 | - | - |
ruRoberta-large finetune | 0.571 | - | - |
ruT5-large-finetune | 0.669 | - | - |
Baseline TF-IDF1.1 | 0.662 | RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark | |
Human Benchmark | 0.84 | RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark | |
RuGPT3Large | 0.636 | - | - |
RuGPT3XL few-shot | 0.649 | - | - |
Golden Transformer | 0.545 | - | - |
Multilingual Bert | 0.669 | - | - |
SBERT_Large_mt_ru_finetuning | 0.675 | - | - |
MT5 Large | 0.669 | mT5: A massively multilingual pre-trained text-to-text transformer | |
RuGPT3Medium | 0.669 | - | - |
heuristic majority | 0.669 | Unreasonable Effectiveness of Rule-Based Heuristics in Solving Russian SuperGLUE Tasks | - |
YaLM 1.0B few-shot | 0.669 | - | - |
Random weighted | 0.597 | Unreasonable Effectiveness of Rule-Based Heuristics in Solving Russian SuperGLUE Tasks | - |
RuGPT3Small | 0.669 | - | - |
SBERT_Large | 0.662 | - | - |
ruBert-large finetune | 0.669 | - | - |
ruBert-base finetune | 0.669 | - | - |
majority_class | 0.669 | Unreasonable Effectiveness of Rule-Based Heuristics in Solving Russian SuperGLUE Tasks | - |
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