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Linguistic Acceptability On Cola

Metrics

Accuracy
MCC

Results

Performance results of various models on this benchmark

Paper Title
En-BERT + TDA + PCA88.6%-Acceptability Judgements via Examining the Topology of Attention Maps
BERT+TDA88.2%0.726Can BERT eat RuCoLA? Topological Data Analysis to Explain
RoBERTa+TDA87.3%0.695Can BERT eat RuCoLA? Topological Data Analysis to Explain
deberta-v3-base+tasksource87.15%-tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation
RoBERTa-large 355M + Entailment as Few-shot Learner86.4%-Entailment as Few-Shot Learner
LTG-BERT-base 98M82.7-Not all layers are equally as important: Every Layer Counts BERT
ELC-BERT-base 98M82.6-Not all layers are equally as important: Every Layer Counts BERT
En-BERT + TDA82.1%0.565Acceptability Judgements via Examining the Topology of Attention Maps
FNet-Large78%-FNet: Mixing Tokens with Fourier Transforms
LTG-BERT-small 24M77.6-Not all layers are equally as important: Every Layer Counts BERT
ELC-BERT-small 24M76.1-Not all layers are equally as important: Every Layer Counts BERT
T5-11B70.8%-Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
StructBERTRoBERTa ensemble69.2%-StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding
ALBERT69.1%-ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
FLOATER-large69%-Learning to Encode Position for Transformer with Continuous Dynamical Model
XLNet (single model)69%-XLNet: Generalized Autoregressive Pretraining for Language Understanding
RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned)68.6%-LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
MT-DNN68.4%-Multi-Task Deep Neural Networks for Natural Language Understanding
ELECTRA68.2%--
RoBERTa (ensemble)67.8%-RoBERTa: A Robustly Optimized BERT Pretraining Approach
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Linguistic Acceptability On Cola | SOTA | HyperAI