MRPC

Overview edit

Variants: MRPC, MRPC Dev

Associated Benchmarks

This dataset is used in 3 benchmarks:

• Few-Shot Learning - Metrics: F1-score
• Natural Language Inference - Metrics: Acc
• Semantic Textual Similarity - Metrics: Accuracy, F1

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization (2024) -
• LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale (2022) -
• DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing (2021) -
• Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners (2021) -
• AutoBERT-Zero: Evolving BERT Backbone from Scratch (2021) -
• Charformer: Fast Character Transformers via Gradient-based Subword Tokenization (2021) -
• FNet: Mixing Tokens with Fourier Transforms (2021) -
• Entailment as Few-Shot Learner (2021) -
• How to Train BERT with an Academic Budget (2021) -
• Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention (2021) -
• CLEAR: Contrastive Learning for Sentence Representation (2020) -
• Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning (2020) -
• RealFormer: Transformer Likes Residual Attention (2020) -
• A Statistical Framework for Low-bitwidth Training of Deep Neural Networks (2020) -
• Big Bird: Transformers for Longer Sequences (2020) -
• SqueezeBERT: What can computer vision teach NLP about efficient neural networks? (2020) -
• MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices (2020) -
• Learning to Encode Position for Transformer with Continuous Dynamical Model (2020) -
• SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization (2019) -
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (2019) -