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Models/SetFit + OCD

SetFit + OCD

Reported on 9 benchmarks across 3 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing6 results

Text ClassificationonSST-5
Accuracy· 2022-10-02
0.478
SOTA
OCD: Learning to Overfit with Conditional Diffusion Models arXiv:2210.00471
Text ClassificationonAmazon Counterfeit
Accuracy· 2022-10-02
0.41
SOTA
OCD: Learning to Overfit with Conditional Diffusion Models arXiv:2210.00471
Few-Shot Text ClassificationonSST-5
Accuracy· 2022-10-02
0.478
SOTA
OCD: Learning to Overfit with Conditional Diffusion Models arXiv:2210.00471
Few-Shot Text ClassificationonAmazon Counterfeit
Accuracy· 2022-10-02
0.41
SOTA
OCD: Learning to Overfit with Conditional Diffusion Models arXiv:2210.00471
Text ClassificationonAverage on NLP datasets
Accuracy· 2022-10-02
0.643
best: 0.648 (SetFit + OCD(5))
OCD: Learning to Overfit with Conditional Diffusion Models arXiv:2210.00471
Few-Shot Text ClassificationonAverage on NLP datasets
Accuracy· 2022-10-02
0.643
best: 0.648 (SetFit + OCD(5))
OCD: Learning to Overfit with Conditional Diffusion Models arXiv:2210.00471

Methodology3 results

ClassificationonSST-5
Accuracy· 2022-10-02
0.478
SOTA
OCD: Learning to Overfit with Conditional Diffusion Models arXiv:2210.00471
ClassificationonAmazon Counterfeit
Accuracy· 2022-10-02
0.41
SOTA
OCD: Learning to Overfit with Conditional Diffusion Models arXiv:2210.00471
ClassificationonAverage on NLP datasets
Accuracy· 2022-10-02
0.643
best: 0.648 (SetFit + OCD(5))
OCD: Learning to Overfit with Conditional Diffusion Models arXiv:2210.00471