三、transformer-based model的应用场景 1. 机器翻译 在机器翻译任务中,transformer-based model以其优秀的性能和能够处理长距离依赖关系的能力,成为了目前最主流的模型架构。 2. 文本生成 在文本生成任务中,transformer-based model能够生成更加流畅、连贯的文本,并且能够控制生成文本的风格和内容。 3. 语言理解 在语言...
Interspeech是世界上规模最大,最全面的顶级语音领域会议,近2000名一线业界和学界人士将会参与包括主题演讲,Tutorial,论文讲解和主会展览等活动,本次阿里论文有8篇入选,本文为Shiliang Zhang, Ming Lei, Zhijie Yan的论文《Investigation of Transformer based Spelling Correction Model for CTC-based End-to-End ...
English |简体中文 Table of Contents Table of Contents Introduction Quick Start How to use Model zoo Algorithm Detailed TODO List Contact Us Reference License Introduction EasyAnimate is a pipeline based on the transformer architecture that can be used to generate AI photos and videos, train baseline...
aSince the model is based on a three winding transformer, three leakage parameters are needed to specify the transformer: x1, x2 and x3 (or x12, x13 and x23). For a normal transformer, however, only one leakage value (x12) is given. In order to compute x1, x2 and x3 from x12, ...
YOLOAir: Make the improvement of the YOLO model faster, more convenient and more complete YOLOAir算法库 是一个基于 PyTorch 的一系列 YOLO 检测算法组合工具箱。用来组合不同模块构建不同网络。 内置YOLOv5、YOLOv7、YOLOX、YOLOR、Transformer、Scaled_YOLOv4、YOLOv3、YOLOv4、YOLO-Facev2、TPH-YOLOv5、YO...
在实验A中,我们只使用了一个带有改进的自我注意的单流模型single-stream model with a modified self-attention,这与原来的FLAT模型相似。区别在于我们使用随机初始化的注意矩阵(随机注意)进行注意计算。我们结合了格嵌入和根级嵌入lattice embedding and radical-level embedding作为模型的输入。目的是验证双流模型相对于...
aThis geometry is not dissimilar to that which develops in atmospheric corrosion, and it has been historically modelled by Evans and recently revisited by Cole et al.According to the Evans model, oxygen reduction is favoured at a droplet edge, as oxygen can readily diffuse to the metal surface...
Since the model is based on a three winding transformer, three leakage parameters are needed to specify the transformer: x1, x2 and x3 (or x12, x13 and x23). For a normal transformer, however, only one leakage value (x12) is given. In order to compute x1, x2 and x3 from x12, tw...
model import Bert, BertConfig from model_center.layer import Linear class BertModel(torch.nn.Module): def __init__(self, config): super().__init__() self.bert = Bert.from_pretrained("bert-base-uncased") self.dense = Linear(config.dim_model, 2) bmt.init_parameters(self.dense) def ...