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