Accumulated Trivial Attention Matters in Vision Transformers on Small Datasets Xiangyu Chen1, Qinghao Hu2, Kaidong Li1, Cuncong Zhong1, Guanghui Wang3∗ 1Department of EECS, University of Kansas, KS, USA 2Institute of Automation, Chinese Academy of Scien...
Browse State-of-the-Art Datasets Methods More Sign In Paper Transformers Meet Visual Learning Understanding: A Comprehensive Review Dynamic attention mechanism and global modeling ability make Transformer show strong feature learning ability. In recent years, Transformer has become comparable to CNNs ...
One way to reach this goal is by modifying the data representation in order to meet certain fairness constraints... L Oneto,M Donini,G Luise,... 被引量: 0发表: 2020年 Deep Transferable Compound Representation Across Domains and Tasks for Low Data Drug Discovery Themain problem of small ...
multi-task learning can be applied to train a model across multiple related tasks and evaluate the NR-activity with different ligands, or to infer the effects of NR-ligands in different tissues [8,9]. In drug discovery, where high-quality labeled information is limited, meta-learning...
[36] have pre-trained GNNs to learn local information and obtain improved performances across various chemical property tasks. Based on this method, Guo et al. [37] proposed a novel meta-learning approach that allows GNNs to fast adapt across tasks using task-specific weights to meet self-...
pip install transformers datasets accelerate tensorboard evaluate --upgrade 在这个例子中,我们使用merve/beans-vit-224模型作为教师模型。这是一个基于google/vit-base-patch16-224-in21k在 beans 数据集上微调的图像分类模型。我们将将这个模型蒸馏到一个随机初始化的 MobileNetV2。
System Info transformers version: 4.36.0 Platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.31 Python version: 3.10.12 Huggingface_hub version: 0.19.4 Safetensors version: 0.3.1 Accelerate version: 0.25.0 Accelerate config: not found P...
自然语言处理(NLP)领域中,文本生成是一项引人注目的任务,它涉及到使用计算机来生成具有自然语言风格和语法的文本。本文将深入研究NLP在文本生成中的原理,介绍常见的技术方法,并提供一个基于Python和现代NLP库的简单实例,以帮助读者更好地理解和应用这一领域的知识。
In addition, if you still can’t find a ready-to-use model tailored exactly to your use case you can take a pre-trained model and fine-tune it (re-train a small part of it) using one of the various datasets containing labeled data focusing on the most common tasks. You can find t...
model_checkpoint = "t5-small" 只要预训练的transformer模型包含seq2seq结构的head层,那么本notebook理论上可以使用各种各样的transformer模型,解决任何摘要生成任务。这里,我们使用t5-small模型checkpoint。 加载数据 我们将会使用Datasets库来加载数据和对应的评测方式。数据加载和评测方式加载只需要简单使用load_dataset和...