AventIQ-AI/Zero-shot-Text-Classification-Model

Zero-Shot Text Classification using facebook/bart-large-mnli

This repository demonstrates how to use the facebook/bart-large-mnli model for zero-shot text classification based on natural language inference (NLI).

We extend the base usage by:

• Using a labeled dataset for benchmarking
• Performing optional fine-tuning
• Quantizing the model to FP16
• Scoring model performance

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📌 Model Description

• Model: facebook/bart-large-mnli
• Type: NLI-based zero-shot classifier
• Architecture: BART (Bidirectional and Auto-Regressive Transformers)
• Usage: Classifies text by scoring label hypotheses as NLI entailment

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📂 Dataset

We use the yahoo_answers_topics dataset from Hugging Face for evaluation. It contains questions categorized into 10 topics.

from datasets import load_dataset

dataset = load_dataset("yahoo_answers_topics")

🧠 Zero-Shot Classification Logic

The model checks whether a text entails a hypothesis like:

"This text is about sports."

For each candidate label (e.g., "sports", "education", "health"), we convert them into such hypotheses and use the model to score them.

✅ Example: Inference with Zero-Shot Pipeline

from transformers import pipeline

classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

sequence = "The team played well and won the championship."
labels = ["sports", "politics", "education", "technology"]

result = classifier(sequence, candidate_labels=labels)
print(result)

📊 Scoring / Evaluation

Evaluate zero-shot classification using accuracy or top-k accuracy:

from sklearn.metrics import accuracy_score

def evaluate_zero_shot(dataset, labels):
    correct = 0
    total = 0
    for example in dataset:
        result = classifier(example["question_content"], candidate_labels=labels)
        predicted = result["labels"][0]
        true = labels[example["topic"]]
        correct += int(predicted == true)
        total += 1
    return correct / total

labels = ["Society & Culture", "Science & Mathematics", "Health", "Education", 
          "Computers & Internet", "Sports", "Business & Finance", "Entertainment & Music", 
          "Family & Relationships", "Politics & Government"]

acc = evaluate_zero_shot(dataset["test"].select(range(100)), labels)
print(f"Accuracy: {acc:.2%}")