Install Required Libraries
To begin, we need to install the necessary Python libraries. Hugging Face’stransformers
library provides state-of-the-art machine learning models, while the torch
library supports these models.
Execute the following command in your terminal to install the required libraries:
torch
and transformers
libraries. torch
is used for creating and running models, and transformers
provides pre-trained models.
Import libraries
Next, we need to import the libraries into our Python script. Create a new Python file namedsentiment_analysis.py
and include the following import statements:
sentiment_analysis.py
runpod
SDK for serverless functions and the pipeline
method from transformers
, which allows us to use pre-trained models.
Load the Model
Loading the model in a function ensures that the model is only loaded once when the worker starts, optimizing the performance of our application. Add the following code to yoursentiment_analysis.py
file:
sentiment_analysis.py
pipeline
method from transformers
to load a pre-trained sentiment analysis model. The distilbert-base-uncased-finetuned-sst-2-english
model is a distilled version of BERT fine-tuned for sentiment analysis tasks.
Define the Handler Function
We will now define the handler function that will process incoming events and use the model for sentiment analysis. Add the following code to your script:sentiment_analysis.py
- Ensures the model is loaded.
- Retrieves the input text from the incoming event.
- Validates the input to ensure text is provided.
- Uses the loaded model to perform sentiment analysis.
- Returns the sentiment label and score as a dictionary.
Start the Serverless Worker
To run our sentiment analysis function as a serverless worker, we need to start the worker using Runpod’s SDK. Add the following line at the end of yoursentiment_analysis.py
file:
sentiment_analysis.py
sentiment_analysis_handler
as the handler function for incoming requests.
Complete Code
Here is the complete code for our sentiment analysis serverless function:sentiment_analysis.py
Testing Locally
To test this function locally, create a file namedtest_input.json
with the following content:
test_input.json