浅析Self-Attention、ELMO、Transformer、BERT、ERNIE、GPT、ChatGPT等NLP models 笑个不停 数据挖掘小朋友186 人赞同了该文章 目录 收起 一、self-attention 1、整体架构 2、如何计算input向量之间的关联性 3、常用的α计算方法 4、self-attention的详细框架 5、从矩阵计算的角度来看self-attention 6、...
https://medium.com/@sntaus/understanding-self-attention-gpt-models-80ec894eebf0 https://jalammar.github.io/illustrated-gpt2/
Attention机制最早是在视觉图像领域提出来的,应该是在九几年思想就提出来了,但是真正火起来应该算是2014年google mind团队的这篇论文《Recurrent Models of Visual Attention》,他们在RNN模型上使用了attention机制来进行图像分类。随后,Bahdanau等人在论文《Neural Machine Translation by Jointly Learning to Align and Tra...
也是Google在2023年，于GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpo...
Surprisingly, this simple architecture outperforms more complex models that mix convolutional, recurrent and attentive layers. KWT can be used as a drop-in replacement for these models, setting two new benchmark records on the Google Speech Commands dataset with 98.6% and 97.7% accuracy on the ...
('x_test shape:', x_test.shape)#%%batch_size = 32from keras.models import Modelfrom keras.optimizers import SGD,Adamfrom keras.layers import *from Attention_keras import Attention,Position_EmbeddingS_inputs = Input(shape=(64,), dtype='int32')embeddings = Embedding(max_features, 128)(S_...
The models proposed recently for neural machine translation often belong to a family of encoder–...
Attention 机制最早是在视觉图像领域提出来的,应该是在九几年思想就提出来了,但是真正火起来应该算是 2014 年 Google Mind 团队的这篇论文Recurrent Models of Visual Attention[4],他们在 RNN 模型上使用了 Attention机制来进行图像分类。 随后,Bahdanau 等人在论文Neural Machine Translation by Jointly Learning to ...
Liu, Bing, and Ian Lane. "Attention-based recurrent neural network models for joint intent detection and slot filling." arXiv preprint arXiv:1609.01454 (2016). Goo, Chih-Wen, et al. "Slot-gated modeling for joint slot filling and intent prediction." Proceedings of the 2018 Conference of th...
Attention机制最早是在视觉图像领域提出来的,应该是在九几年思想就提出来了,但是真正火起来应该算是2014年google mind团队的这篇论文《Recurrent Models of Visual Attention》,他们在RNN模型上使用了attention机制来进行图像分类。随后,Bahdanau等人在论文《Neural Machine Translation by Jointly Learning to Align and Tra...