Python Coding challenge - Day 403| What is the output of the following Python Code?

 

Code Explanation:

Step 1: Importing the pipeline from transformers

from transformers import pipeline

The pipeline function from the transformers library simplifies access to various pre-trained models.

Here, it's used for sentiment analysis.

Step 2: Creating a Sentiment Analysis Pipeline

classifier = pipeline("sentiment-analysis")

This automatically loads a pre-trained model for sentiment analysis.

By default, it uses distilbert-base-uncased-finetuned-sst-2-english, a model trained on the Stanford Sentiment Treebank (SST-2) dataset.

The model classifies text into positive or negative sentiment.

Step 3: Running Sentiment Analysis on a Given Text

result = classifier("I love AI and Python!")

The classifier analyzes the input "I love AI and Python!" and returns a list of dictionaries containing:

label: The predicted sentiment (POSITIVE or NEGATIVE).

score: The confidence score of the prediction (between 0 and 1).

Example output:

[{'label': 'POSITIVE', 'score': 0.9998}]

Here, the model predicts POSITIVE sentiment with a high confidence score.

Step 4: Extracting and Printing the Sentiment Label

print(result[0]['label'])

Since result is a list with a single dictionary, result[0] gives:

{'label': 'POSITIVE', 'score': 0.9998}

result[0]['label'] extracts the sentiment label, which is "POSITIVE".

Final Output:

POSITIVE