Metaphor Detection& interpretation with VUA20
Budget: ₹1,500 – ₹12,500 INR
I am working with the VUA20 dataset on Hugging Face and have a baseline metaphor-detection model in place. I now want to refine that system, turn it into a stronger paraphrase-aware model, and benchmark it against two large-language-model backbones—BERT and RoBERTa.
The work I need completed is three-fold:
1. Data & Model
• Re-train or fine-tune BERT and RoBERTa on the VUA20.
• Integrate a paraphrasing component so the model not only flags a metaphor but can restate the sentence in literal form.
2. Explanation Layer
• For every detected metaphor, generate a concise textual explanation describing why the phrase is figurative and how the paraphrase conveys the literal meaning.
3. Evaluation & Comparison
• Report Accuracy, Recall and F1 Score for each model.
• Summarise gains or trade-offs versus my existing baseline.
Deliverables
• Clean, reproducible code (Python, PyTorch or TensorFlow).
• A short technical report with the metric tables and discussion.
• Sample output file showing original sentence, model decision, explanation, and paraphrase.
• README with setup instructions and commands to replicate the results.
If anything in the dataset handling or metric calculation needs special care, note it in the documentation so I can audit it quickly. Looking forward to seeing how far we can push metaphor detection with these architectures.
The work I need completed is three-fold:
1. Data & Model
• Re-train or fine-tune BERT and RoBERTa on the VUA20.
• Integrate a paraphrasing component so the model not only flags a metaphor but can restate the sentence in literal form.
2. Explanation Layer
• For every detected metaphor, generate a concise textual explanation describing why the phrase is figurative and how the paraphrase conveys the literal meaning.
3. Evaluation & Comparison
• Report Accuracy, Recall and F1 Score for each model.
• Summarise gains or trade-offs versus my existing baseline.
Deliverables
• Clean, reproducible code (Python, PyTorch or TensorFlow).
• A short technical report with the metric tables and discussion.
• Sample output file showing original sentence, model decision, explanation, and paraphrase.
• README with setup instructions and commands to replicate the results.
If anything in the dataset handling or metric calculation needs special care, note it in the documentation so I can audit it quickly. Looking forward to seeing how far we can push metaphor detection with these architectures.