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Models/ERNIE 2.0

ERNIE 2.0

Reported on 8 benchmarks across 2 tasks · 1 paper · 4 SOTA

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

Natural Language Processing4 results

Text ClassificationonTwitter
Accuracy· 2022-11-30
99.97
SOTA
Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets arXiv:2211.16878
Text ClassificationonSTOPS-2
STOPS-2· 2022-11-30
99.88
SOTA
Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets arXiv:2211.16878
Text ClassificationonR8
Accuracy· 2022-11-30
98.041
best: 98.451 (DeBERTa)
Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets arXiv:2211.16878
Text ClassificationonMR
Accuracy· 2022-11-30
88.97
best: 93.3 (VLAWE)
Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets arXiv:2211.16878

Methodology4 results

ClassificationonTwitter
Accuracy· 2022-11-30
99.97
SOTA
Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets arXiv:2211.16878
ClassificationonSTOPS-2
STOPS-2· 2022-11-30
99.88
SOTA
Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets arXiv:2211.16878
ClassificationonR8
Accuracy· 2022-11-30
98.041
best: 98.451 (DeBERTa)
Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets arXiv:2211.16878
ClassificationonMR
Accuracy· 2022-11-30
88.97
best: 93.3 (VLAWE)
Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets arXiv:2211.16878