The GNMT v2 model is similar to the one discussed in theGoogle's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationpaper. The most important difference between the two models is in the attention mechanism. In our model, the output from the first LSTM layer...
g3doc scripts standard_hparams testdata utils .gitignore __init__.py attention_model.py gnmt_model.py inference.py inference_test.py model.py model_helper.py model_test.py nmt.py nmt_test.py train.py CONTRIBUTING.md LICENSE README.md...
我们首先需要了解构建一个 NMT 模型具体代码的核心,我们会在图 2 中更详细地讲解。我们后面会介绍数据准备和全部的代码,这一部分是指 model.py 文件。 在网络的底层,编码器和解码器 RNN 接收到以下输入:首先是原句子,然后是从编码到解码模式的过渡边界符号「」,最后是目标语句。对于训练来说,我们将为系统提供以...
The present invention is a new type of Glycine N-methyltransferase (GNMT) knockout mice model. This model can be applied to screen drug, test of treatment and search for diagnostic marker of hepatocellular carcinoma (HCC), glycogen storage disease, liver dysplasia, fatty liver and other liver ...
训练之后,我们可以使用带有新 model_dir 的相同推理指令进行推理: python-mnmt.nmt\--model_dir=/tmp/nmt_attention_model\--inference_input_file=/tmp/my_infer_file.vi\--inference_output_file=/tmp/nmt_attention_model/output_infer 基准IWSLT 英语-越南语 训练:133k 的样本,dev=tst2012,test=tst2013 ...
采用更好的wordpiece model,也叫sub-word units,比如在“Turing’s major is NLP .”一句经过WPM模型处理之后应该是"Turing ‘s major is NLP ." 另外为了直接复制人名等词,使source language和target language共享wordpiece model,WPM在单词的灵活性和准确性上取得了一个很好的均衡,也在翻译有更好的准确率(BLEU...
谷歌采用两种方法,第一种是创建了wordpiece model,例子如下图: 上图中,’_’用于标记字的开始位置。 谷歌指出,使用8k-32k大小的字典能带来较好的BLEU成绩和编码效率。 第二种则是Mixed Word/Character Model,将生词分割成字,并使用特殊字符指定位置。例如<B>,<M>,<E>分别表示词的开始,中间,末尾三中不同的位置...
https://docs.openvinotoolkit.org/2019_R3.1/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_GNMT_From_Tensorflow.html#patch-file However, when I run the following command: python3 benchmark_app.py -i test.txt -m frozen_GNMT_inference_graph.xml -d CPU I see this error: [ ER...
由于attention wrapper,就不再需要扩展我们带有 attention 的 vanilla seq2seq 代码。这部分文件为 attention_model.py。 首先,我们需要定义一种注意机制,例如采用 Luong et al., 2015 的研究。 # attention_states: [batch_size, max_time, num_units] attention_states = tf.transpose(encoder_outputs, [1, ...
This repository provides a script and recipe to train the GNMT v2 model to achieve state-of-the-art accuracy and is tested and maintained by NVIDIA. GNMT model for TensorFlow1 is no longer maintained and will soon become unavailable, please consider PyTorch or TensorFlow2 models as...