Example: Question Answering

Last updated 1 year ago

Question answering models can retrieve the answer to a question from a given text, which is useful for searching for an answer in a document.

Throughout the guide, we will use a simple question answering model based on 🤗

This downloads a default pretrained model and tokenizer for Questioning Answering. Now you can use the qa_model on your target question / context:

Extract Embeddings

To extract embeddings from the model, we'll first need to do two things:

Pass output_hidden_states=True to our model params.
When we call pipeline(...) it does a lot of things for us - preprocessing, inference, and postprocessing. We'll need to break all this, so we can interfere in the middle and get embeddings

In other words:

And finally, to extract embeddings for this prediction:

For example, you could use a Parquet file on S3 or a Postgres table that looks like this:

Now let’s add some monitoring to this model 🚀 To monitor this model in Aporia, the first step is to create a model version:

Next, we can log predictions directly to Aporia:

Your model should now be integrated to Aporia! 🎉

Last updated 1 year ago