在融合Bert与GCN训练这部分,文中指出,将Bert encoder部分得到embedding后丢进GCN里,直接联合训练,会有两个问题出现,1. 梯度回传时,Bert部分得不到有效的梯度优化。2. GCN是全图更新的,假设图是1w个document节点,则bert部分1w个document同时进行bert encoder得到document embedding,然后丢到GCN layer中更新训练,这显然...
--encoder --layer: 56,721,408 91.97% 216.38 --cls --predictions(partially shared): 597,279 0.97% 2.28 --bias: 5,151 0.01% 0.02 --transform: 592,128 0.96% 2.26 --decoder(shared): 0 0.00% 0.00 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18...
通常decoder-only结构(比如GPT)的训练目标是Language Modeling,即LM objective,而encoder-only结构(比如BERT)和encoder-decoder结构(比如T5)的训练目标通常是Denoising,即Denoising objective。但是为了对比不同训练目标对最后下游任务的影响,作者做了一些交叉实验,如下表所示,最终结果证明了Denoising objective从整体上看还是要...
ELECTRA:ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators TextGCN:Graph Convolutional Networks for Text Classification 序列标注(SL, sequence-labeling) CRF:Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data ...
model=TextRNN()criterion=nn.CrossEntropyLoss()optimizer=optim.Adam(model.parameters(),lr=0.001) 以上代码每一步都值得说一下,首先是nn.RNN(input_size, hidden_size)的两个参数,input_size表示每个词的编码维度,由于我是用的one-hot编码,而不是WordEmbedding,所以input_size就等于词库的大小len(vocab),即n...
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators - google-research/electra
[2022/11/01 10:48:06] ppocr INFO: encoder_type : rnn [2022/11/01 10:48:06] ppocr INFO: hidden_size : 64 [2022/11/01 10:48:06] ppocr INFO: name : SequenceEncoder [2022/11/01 10:48:06] ppocr INFO: Transform : None [2022/11/01 10:48:06] ppocr INFO: algorithm : C...
from sklearn.preprocessing import LabelEncoder,OneHotEncoder # 用于对数据集的标签进行编码 from keras.models import Model # 通用模型定义方法 from keras import Sequential # 序列模型定义方法 from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding # keras中添加的各层 ...
However, as discussed in Chapter 1, the original Transformer architecture actually consists of an encoder-decoder architecture. Like the decoder-only models, these encoder-decoder models are sequence-to-sequence models and generally fall in the category of generative models. An interesting family of ...
length_tensor, input_tensor, text_gt = self.label_encoder(label) hr_pred, word_attention_map_gt, hr_correct_list = self.transformer(to_gray_tensor(hr_img), length_tensor, input_tensor, test=False) sr_pred, word_attention_map_pred, sr_correct_list = self.transformer(to_gray_tensor(sr...