Create README.md

README.md

@@ -0,0 +1,75 @@
+# 🎯 Tone Detection using `facebook/bart-large-mnli` (Zero-Shot Classification)
+
+This project demonstrates how to perform **Tone Detection** using the [`facebook/bart-large-mnli`](https://huggingface.co/facebook/bart-large-mnli) model through **zero-shot classification** based on Natural Language Inference (NLI).
+
+This approach enables you to classify emotional tone (e.g., joy, anger, sadness) **without training**, by framing it as a textual entailment task.
+
+---
+
+## 📌 Model Details
+
+- **Model:** `facebook/bart-large-mnli`
+- **Task:** Zero-shot classification via NLI
+- **Approach:** Checks if the input sentence entails a hypothesis (e.g., "This text expresses anger.")
+- **Strength:** No labeled training data required
+
+---
+
+## 📂 Dataset Used
+
+For benchmarking and scoring, we use the [`go_emotions`](https://huggingface.co/datasets/go_emotions) dataset:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("go_emotions")
+```
+
+# 🧠 Tone Detection (Inference)
+```Python
+from transformers import pipeline
+
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+
+labels = ["joy", "anger", "sadness", "fear", "surprise", "neutral"]
+
+text = "I can't believe this is happening again. So frustrating."
+
+result = classifier(text, candidate_labels=labels, hypothesis_template="This text expresses {}.")
+print(result)
+```
+
+# 🧪 Evaluation with Scoring
+
+```python
+from sklearn.metrics import accuracy_score
+
+# Mapping GoEmotions label indices to names
+id2label = dataset["train"].features["labels"].feature.names
+
+# Evaluate on a small sample
+def evaluate(dataset, candidate_labels):
+    correct = 0
+    total = 0
+    for row in dataset.select(range(100)):  # Use more samples as needed
+        text = row["text"]
+        true_labels = [id2label[i] for i in row["labels"]]
+        result = classifier(text, candidate_labels=candidate_labels, hypothesis_template="This text expresses {}.")
+        predicted = result["labels"][0]
+        if predicted in true_labels:
+            correct += 1
+        total += 1
+    return correct/total
+
+accuracy = evaluate(dataset["test"], candidate_labels=labels)
+print(f"Zero-shot Accuracy: {accuracy:.2%}")
+```
+
+# ⚙️ Use Cases
+Customer support tone analysis
+
+Chat moderation for emotional tone
+
+Feedback sentiment detection
+
+Real-time conversation emotion tagging