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Few Shot Text Classification On Raft

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Résultats

Résultats de performance de divers modèles sur ce benchmark

Paper Title
T-Few0.950.8040.7580.6950.8330.6760.9150.5080.7360.8790.5860.75Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
GPT-30.9370.6860.6270.2990.6790.4310.7690.5160.6560.8210.5260.574RAFT: A Real-World Few-Shot Text Classification Benchmark
Human (crowdsourced)0.9170.8300.7350.6070.8570.6460.9080.4680.6090.8970.7220.627RAFT: A Real-World Few-Shot Text Classification Benchmark
AdaBoost0.8380.5430.5140.0230.6260.4750.4550.5060.5560.6250.4430.560RAFT: A Real-World Few-Shot Text Classification Benchmark
GPT-Neo0.6810.4520.4810.1490.4080.3430.4060.4930.6050.6360.5540.565RAFT: A Real-World Few-Shot Text Classification Benchmark
GPT-20.4980.6000.4580.1210.5610.2450.3800.4920.6120.7230.3110.498RAFT: A Real-World Few-Shot Text Classification Benchmark
BART MNLI zero-shot0.4620.2340.3820.3320.6150.3600.6440.0260.4690.4000.5430.122RAFT: A Real-World Few-Shot Text Classification Benchmark
GPT-3 zero-shot0.3780.1630.2920.0000.5720.3230.6280.0270.3620.2900.3030.164RAFT: A Real-World Few-Shot Text Classification Benchmark
Plurality-class0.3370.4460.3310.0000.3530.1640.2710.4930.3440.3910.3660.471RAFT: A Real-World Few-Shot Text Classification Benchmark
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Few Shot Text Classification On Raft | SOTA | HyperAI