Best Accuracy
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BERT-Large set new accuracy records on eleven NLP tasks, including question answering and language inference.
DINOv2 with ViT-g sets new accuracy records for self-supervised visual feature learning on multiple downstream tasks.
Professional 48-inch level with rare earth vials for high accuracy. Lightweight aluminum frame with shock-proof end caps.
Llama 3 70B is a powerful open-source large language model developed by Meta. It distinguishes itself through its massive training dataset and optimized architecture, resulting in exceptional performa...
Vision Transformer Large achieves competitive accuracy on ImageNet by applying transformer architecture directly to image patches.
Google's PaLM 540B achieves breakthrough accuracy across reasoning, language understanding, and generation tasks.
RoBERTa-Large improves upon BERT with more training data and longer training, achieving higher accuracy on GLUE and other benchmarks.
Swin-L introduces shifted windows for efficient attention, achieving top accuracy on ImageNet and other vision tasks.
Google's T5-11B achieves high accuracy across diverse NLP tasks via a unified text-to-text framework.
ConvNeXt-XL modernizes the standard ConvNet to achieve accuracy competitive with vision transformers on ImageNet.
Noisy Student training with EfficientNet-L2 achieves state-of-the-art accuracy on ImageNet using self-training.
Google's GLaM achieves high accuracy with a sparse mixture-of-experts architecture, surpassing dense models on several benchmarks.
Baidu's ERNIE 3.0 Titan achieves high accuracy on Chinese and English benchmarks by incorporating knowledge graph embeddings.
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