Inference using Pipeline and TensorFlow
Is there a way to use a Dataset during inference with a standard Pipeline (zero-shot-classification task) with TensorFlow? I found a note about this in the Pipeline documentation, but it seems to only be applicable to PyTorch with the KeyDataset object -PipelinesThe reason behind my question is I have application where I am passed Pandas DataFrames of text that I need to classify using my zero-shot-classification model. My thought was for fastest inference I should be utilizing the Dataset object rather than converting my text data to a list, however it is unclear to me if I can then use the Pipeline layer on the Dataset in TensorFlow or if I need to instead tokenize the Dataset separately and then have logic to process my data through the model without using the Pipeline. I’m looking for the most efficient solution.Also, just a few more details about my constraints – as mentioned I am using TensorFlow not PyTorch, the inference is being run on CPU, and cloud/api solutions are not an option.