HomeAIFinish-to-Finish NLP Challenge with Hugging Face, FastAPI, and Docker | by Kasper...

Finish-to-Finish NLP Challenge with Hugging Face, FastAPI, and Docker | by Kasper Groes Albin Ludvigsen | Mar, 2024


This tutorial explains the best way to construct a containerized sentiment evaluation API utilizing Hugging Face, FastAPI and Docker

Photograph by Joshua Hoehne on Unsplash

Many AI initiatives fail, in accordance with varied stories (eg. Hardvard Enterprise Evaluation). I speculate that a part of the barrier to AI undertaking success is the technical step from having constructed a mannequin to creating it broadly accessible for others in your group.

So how do you make your mannequin simply accessible for consumption? A method is to wrap it in an API and containerize it in order that your mannequin will be uncovered on any server with Docker put in. And that’s precisely what we’ll do on this tutorial.

We are going to take a sentiment evaluation mannequin from Hugging Face (an arbitrary alternative simply to have a mannequin that’s straightforward to indicate for instance), write an API endpoint that exposes the mannequin utilizing FastAPI, after which we’ll containerize our sentiment evaluation app with Docker. I’ll present code examples and explanations all the way in which.

The tutorial code has been examined on Linux, and will work on Home windows too.

We are going to use the Pipeline class from Hugging Face’s transformers library. See Hugging Face’s tutorial for an introduction to the Pipeline in case you’re unfamiliar with it.

The pipeline makes it very straightforward to make use of fashions corresponding to sentiment fashions. Try Hugging Face’s sentiment evaluation tutorial for a radical introduction to the idea.

You may instantiate the pipe with a number of completely different constructor arguments. A method is to move in a sort of activity:

from transformers import pipeline

pipe = pipeline(activity="sentiment-analysis")

This may use Hugging Face’s default mannequin for the supplied activity.

One other method is to move the mannequin argument specifying which mannequin you need to use. You don’t…



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