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XforcekeygenRevit2017activation



 


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] process, which is designed to be intuitive to reproduce and well scalable on our GPU platform. In this work, we show that we can outperform these baseline models on the task of predicting the set of tokens in an utterance. Setup ----- We train our model on a 12 GPU cluster with 2 Nvidia P100 GPUs per node. All models are trained using an Adam optimizer with a learning rate of $1e-4$ for up to 100 epochs, and a patience of 5. Model predictions are validated every 10 epochs using a validation set of 300 tokens. The Neural Network Model ----------------------- Our model architecture is based on the Transformer architecture [@vaswani2017attention] for language modeling. We use an embedding layer of size $512$ followed by a multi-head attention (MH) layer with a hidden size of $2,048$ and 4 attention heads. We use self-attention along with residual connections to enable information flow across input tokens. The output of the MH layer is fed into two self-attention layers with a hidden size of $2,048$. These layers are also followed by residual connections and fully connected layers with a hidden size of $2,048$. Experiments =========== We present results on the WMT shared tasks, single-sentence classification, and automatic evaluation of dialogue systems for the [De-En]{} and [En-Fr]{} languages [@massialas2018modeling]. We train our models using the same training setup as @ren2019fast, with the only modification being that we disable dropout during fine-tuning to prevent overfitting. Our models are available at []{} for download. Datasets -------- Our datasets for [De-En]{} and [En-Fr]{} are two language pairs in the WMT’18 shared task on neural machine translation. For the [

 

 


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XforcekeygenRevit2017activation

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