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README.md

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+# Zero-Shot Text Classification using `facebook/bart-large-mnli`
+
+This repository demonstrates how to use the [`facebook/bart-large-mnli`](https://huggingface.co/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
+
+---
+
+## 📌 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
+
+---
+
+## 📂 Dataset
+
+We use the [`yahoo_answers_topics`](https://huggingface.co/datasets/yahoo_answers_topics) dataset from Hugging Face for evaluation. It contains questions categorized into 10 topics.
+
+```python
+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
+```python
+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:
+
+```python
+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%}")
+```