--target-out generate.hyp.processed
```
Now we have the final set of reports in "generate.hyp.processed", with "\" replaced by the original word from the input sequence.
Model Deployment
PyTorch offers great flexibility in modeling and a rich surrounding ecosystem. However, while several recent articles have suggested that the use of PyTorch in research and academia may be close to surpassing TensorFlow, there seems to be an overall sense of TensorFlow being the preferred platform for deployment to production. Is this still the case in 2021? Teams looking to serve their PyTorch models in production have a few options.
Before describing our journey, let's take a brief detour and define the term model.
Models as computation graphs