Building Zero Shot Classifiers For Text Using Large Language Models

The software tools we need for the activity covered in this post are:

1. Google Colab
2. GitHub
   
3. And last: ChatGPT

Why I needed these three items?

I needed Google Colab to write code. Google Colab allowed to me not care about creating a local environment and setting it up with the required packages such as 'transformers' from Hugging Face.

I needed GitHub to put my code in a place that I can make available to myself anywhere and also to you (my readers).

I needed ChatGPT to get boilerplate code for our particular task. I learnt about the prompts I needed for this activity from the book by Sinan Ozdemir titled: 

Quick Start Guide to Large Language Models. Strategies and Best Practices for using ChatGPT and Other LLMs - Addison-Wesley Professional (2023)

Download Book

What you would need would (I think) is:

Google Colab that is connected for code to my public repository hosted on GitHub.

 
How to connect Google Colab with GitHub?

1. Once you would open the Google Colab, you get a screen as shown below.

Note that: I am logged in to my Google Account for this task.

2. Next: you would click on "File" at the top left.

You would click on "Open Notebook".

And you would select "GitHub" as shown below:

3. You would fill in the username. It is: ashishjain1547

Once you fill in the username, the "Repository" dropdown would auto populate with public repositories available for that user.

The repository you would select is: "generative_ai_workspace_2024_04_05"

4. Once repo is selected, it's notebooks start appearing below:

Code for zero shot Spam vs. Not Spam classifier using Facebook's BART

from transformers import pipeline
def classify_text (email):
    """
    Use Facebook's BART model to classify an email into "spam" or "not spam"
    Args:
    email (str): The email to classify
    Returns:
    str: The classification of the email
    """

    classifier = pipeline('zero-shot-classification', model='facebook/bart-large-mnli')
    labels = ['spam', 'not spam']
    hypothesis_template = 'This email is {}.'
    results = classifier(email, labels, hypothesis_template=hypothesis_template)
    return results['labels'][0]

 

Usage:

 

How we used ChatGPT?

Provide code to do zero shot sentiment analysis using Google BERT.

 

Answer:

To perform zero-shot sentiment analysis using Google's BERT model, you can utilize the Hugging Face Transformers library along with the pre-trained BERT model. Here's how you can do it in Python:

 

from transformers import pipeline

# Load the zero-shot classification pipeline with BERT
classifier = pipeline("zero-shot-classification", model="bert-base-uncased")

# Define a list of texts for sentiment analysis
texts = [
    "I love this product, it's amazing!",
    "This movie was terrible, I hated it.",
    "The weather today is fantastic.",
    "I feel neutral about this situation."
]

# Define the possible labels/categories for sentiment
candidate_labels = ["positive", "negative", "neutral"]

# Perform zero-shot sentiment analysis
results = classifier(texts, candidate_labels)

# Print the results
for i, text in enumerate(texts):
    print(f"Text: {text}")
    print("Sentiment probabilities:")
    for label, score in zip(results[i]['labels'], results[i]['scores']):
        print(f"{label}: {score:.4f}")
    print()
 

 

Output

Text: I love this product, it's amazing!
Sentiment probabilities:
positive: 0.4718
negative: 0.2679
neutral: 0.2603

Text: This movie was terrible, I hated it.
Sentiment probabilities:
negative: 0.3644
positive: 0.3179
neutral: 0.3177

Text: The weather today is fantastic.
Sentiment probabilities:
positive: 0.4026
negative: 0.3039
neutral: 0.2935

Text: I feel neutral about this situation.
Sentiment probabilities:
neutral: 0.3587
negative: 0.3248
positive: 0.3166

Additional Note

Question:

How does the BERT know that the problem is of sentiment analysis and not spam-vs-not_spam?

Answer: 

In the code provided, the BERT model doesn't inherently "know" that the problem is specifically about sentiment analysis. Instead, it is designed for zero-shot classification, which means it can classify texts into predefined categories without being explicitly trained on those categories.

The key lies in the input data and the labels provided. In the example, we've defined a list of texts that contain statements or reviews, and we've also provided a list of candidate labels (positive, negative, neutral) that represent sentiment categories. When you pass these texts and labels to the BERT model through the zero-shot classification pipeline, it infers the sentiment of each text based on its understanding of the language and the provided labels.

If you were to perform a different classification task, such as spam vs. not spam, you would simply replace the list of candidate labels with the appropriate labels for that task (e.g., spam, not_spam). The model doesn't need to be explicitly told the nature of the classification problem because it learns to make predictions based on the provided labels during training.