Pouvez-vous, s'il vous plaît, rectifier cela ? On my colleague, Mrs González Álvarez on I did. We will do the necessary checks, because, of course, the minutes have been adopted Therefore, you will need to make a technical correction in your case. We will see what nevertheless encourage, and so name against the Russian report, it has taken out objective a credible and receive more to a cautious level. Nous ferons les vérifications qui s'imposent, car, bien entendu, le procès-verbal a été adopté par conséquent, il faudra apporter une correction technique dans votre cas. Perhaps a max-length of 200 is too long, and training the sequences on batches based on varying lengths is necessary. The comparisons to Google translations are listed below, right now the model is good at translating short sentences. The maximum length of the sequence input(output) is 200. I didn't sort the sequences into batches based on their lengths.The dimension of the network is smaller due to a smaller dataset.I've used BatchNormalization instead of LayerNormalization in the paper. The main differences to the models in the paper: After 144 epochs, the categorical crossentropy is reduced to 0.001 Models The training time on a GTX 1080 is 45 hours. To avoid too much training time, only 150000 sentence pairs were used. The dataset used is European Parliament Proceedings Parallel Corpus v7. The architecture of the neural network is (Picture from the paper): It was firstly raised in the paper Neural Machine Translation in Linear Time. Tensorflow v1.1 backend, (Not tested with Theano backend)īyteNet is a character level translation model designed by DeepMind.French to English translator on character level implemented by Keras Dependency
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