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Semantic Textual Similarity On Mrpc

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Résultats

Résultats de performance de divers modèles sur ce benchmark

Paper Title
BigBird91.5Big Bird: Transformers for Longer Sequences
T5-3B92.5Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
MobileBERT-MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
BERT-Base-Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning
Charformer-Tall91.4Charformer: Fast Character Transformers via Gradient-based Subword Tokenization
RoBERTa-large 355M + Entailment as Few-shot Learner91.0Entailment as Few-Shot Learner
Nyströmformer88.1%Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
SMART-BERT-SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned)-LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
FNet-Large-FNet: Mixing Tokens with Fourier Transforms
SqueezeBERT-SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
XLNet (single model)-XLNet: Generalized Autoregressive Pretraining for Language Understanding
SMART-SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
T5-Large92.4Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
TinyBERT-6 67M-TinyBERT: Distilling BERT for Natural Language Understanding
TinyBERT-4 14.5M-TinyBERT: Distilling BERT for Natural Language Understanding
DistilBERT 66M-DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
ERNIE 2.0 Base-ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
T5-Small89.7Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Q8BERT (Zafrir et al., 2019)-Q8BERT: Quantized 8Bit BERT
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