Hi,
I have just finetuned RoBERTa for a classification problem, trained and stored the model. I have used native Tensorflow throughout, but I can’t find any examples anywhere related to finally predicting in TF. The only ones I found were all for PyTorch.
Can someone please guide me to some snippet of code for prediction? The official example has no such code…
EDIT:- I tried using model.predict() method that is used normally and even the PyTorch one which reports missing config. I am really confused as to how we are supposed to serve predictions. IMO the docs need updating
Hi @Neel-Gupta,
The steps are usually as follows
You load your trained model similar to this
loaded_model = TFDistilBertForSequenceClassification.from_pretrained("./sentiment")
Then you usually have to run your text through the preprocessing in the same way you did for your training. Usually some kind of tokenizing involved
predict_input = tokenizer.encode(test_sentence,
truncation=True,
padding=True,
return_tensors="tf")
And then you finally can predict based on your loaded model and the preprocessed text
tf_output = loaded_model.predict(predict_input)[0]
Disclaimer, I am the author of this article, but It covers the process from data to deployment.
https://blog.doit-intl.com/performing-surprisingly-easy-sentiment-analysis-on-google-cloud-platform-fc26b2e2b4b