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Models/BERT-base

BERT-base

Reported on 37 benchmarks across 11 tasks · 6 papers · 2 SOTA

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

Natural Language Processing26 results

Natural Language InferenceonKUAKE-QQR
Accuracy· 2021-06-15
84.7
SOTA
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark arXiv:2106.08087
Relation ExtractiononDocRED
Ign F1· 2019-09-26
56.17
best: 65.47 (DREEAM)
SOTA
Fine-tune Bert for DocRED with Two-step Process arXiv:1909.11898
Text ClassificationonSocial media attributions of YouTube comments
Accuracy (2 classes)· 2024-01-30
0.822
best: 0.8309 (Space-BERT)
Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs arXiv:2401.16638
Text ClassificationonSocial media attributions of YouTube comments
F1 Macro· 2024-01-30
0.7484
best: 0.8006 (Space-BERT)
Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs arXiv:2401.16638
Text ClassificationonHateXplain
Accuracy (2 classes)· 2024-01-30
0.6588
best: 0.8798 (Space-XLNet)
Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs arXiv:2401.16638
Text ClassificationonHateXplain
F1 Macro· 2024-01-30
0.6555
best: 0.8797 (Space-XLNet)
Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs arXiv:2401.16638
Temporal ProcessingonTempEval-3
Strict Detection (Pr.)· 2021-09-30
81.83
best: 96.37 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Temporal ProcessingonTempEval-3
Strict Detection (Re.)· 2021-09-30
79.56
best: 96.37 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Temporal ProcessingonTempEval-3
Relaxed Detection (F1)· 2021-09-30
90.08
best: 100 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Temporal ProcessingonTempEval-3
Relaxed Detection (Pr.)· 2021-09-30
91.37
best: 100 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Temporal ProcessingonTempEval-3
Relaxed Detection (Re.)· 2021-09-30
88.84
best: 100 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Temporal ProcessingonTempEval-3
Strict Detection (F1)· 2021-09-30
80.67
best: 96.37 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Temporal ProcessingonTempEval-3
Type· 2021-09-30
82
best: 90.43 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Temporal Information ExtractiononTempEval-3
Strict Detection (Pr.)· 2021-09-30
81.83
best: 96.37 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Temporal Information ExtractiononTempEval-3
Strict Detection (Re.)· 2021-09-30
79.56
best: 96.37 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Temporal Information ExtractiononTempEval-3
Relaxed Detection (F1)· 2021-09-30
90.08
best: 100 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Temporal Information ExtractiononTempEval-3
Relaxed Detection (Pr.)· 2021-09-30
91.37
best: 100 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Temporal Information ExtractiononTempEval-3
Relaxed Detection (Re.)· 2021-09-30
88.84
best: 100 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Temporal Information ExtractiononTempEval-3
Strict Detection (F1)· 2021-09-30
80.67
best: 96.37 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Temporal Information ExtractiononTempEval-3
Type· 2021-09-30
82
best: 90.43 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Reading ComprehensiononReClor
Test· 2020-02-11
47.3
best: 80.6 (Rational Reasoner / IDOL)
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning arXiv:2002.04326
Relation ExtractiononDocRED
F1· 2019-09-26
53.22
best: 67.53 (DREEAM)
Fine-tune Bert for DocRED with Two-step Process arXiv:1909.11898
Coreference ResolutiononOntoNotes
F1· 2019-08-24
73.9
best: 83.6 (Maverick_mes)
BERT for Coreference Resolution: Baselines and Analysis arXiv:1908.09091
Question AnsweringonSQuAD2.0
EM
72.072
best: 90.939 (IE-Net (ensemble))
Question AnsweringonSQuAD2.0
F1
75.513
best: 93.214 (IE-Net (ensemble))
Sentence CompletiononHONEST
HONEST
1.19
best: 3.33 (BERT-large)

Medical7 results

Information ExtractiononTempEval-3
Strict Detection (Pr.)· 2021-09-30
81.83
best: 96.37 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Information ExtractiononTempEval-3
Strict Detection (Re.)· 2021-09-30
79.56
best: 96.37 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Information ExtractiononTempEval-3
Relaxed Detection (F1)· 2021-09-30
90.08
best: 100 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Information ExtractiononTempEval-3
Relaxed Detection (Pr.)· 2021-09-30
91.37
best: 100 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Information ExtractiononTempEval-3
Relaxed Detection (Re.)· 2021-09-30
88.84
best: 100 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Information ExtractiononTempEval-3
Strict Detection (F1)· 2021-09-30
80.67
best: 96.37 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927
Information ExtractiononTempEval-3
Type· 2021-09-30
82
best: 90.43 (R2R)
BERT got a Date: Introducing Transformers to Temporal Tagging arXiv:2109.14927

Methodology4 results

ClassificationonSocial media attributions of YouTube comments
Accuracy (2 classes)· 2024-01-30
0.822
best: 0.8309 (Space-BERT)
Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs arXiv:2401.16638
ClassificationonSocial media attributions of YouTube comments
F1 Macro· 2024-01-30
0.7484
best: 0.8006 (Space-BERT)
Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs arXiv:2401.16638
ClassificationonHateXplain
Accuracy (2 classes)· 2024-01-30
0.6588
best: 0.8798 (Space-XLNet)
Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs arXiv:2401.16638
ClassificationonHateXplain
F1 Macro· 2024-01-30
0.6555
best: 0.8797 (Space-XLNet)
Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs arXiv:2401.16638