Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language: en
+license: mit
+tags:
+- stance-detection
+- deberta
+- text-classification
+- argument-mining
+metrics:
+- accuracy
+- f1
+model-index:
+- name: debertav3-stance-detection
+  results:
+  - task:
+      type: text-classification
+      name: Stance Detection
+    metrics:
+    - type: accuracy
+      value: 0.9997
+      name: Accuracy
+    - type: f1
+      value: 0.9997
+      name: F1 Score
+---
+
+# Stance Detection with DeBERTa-v3-large
+
+This model detects whether an argument supports (PRO) or opposes (CON) a given topic.
+
+## Model Description
+
+- **Base Model:** microsoft/deberta-v3-large
+- **Task:** Binary stance classification (PRO/CON)
+- **Training Data:** IBM ArgKP-2023 dataset (~32,000 examples)
+- **Calibration:** Label smoothing (0.1) for proper confidence scores
+
+## Performance
+
+- **Test Accuracy:** 99.97%
+- **Test F1 Score:** 99.97%
+- **Mean Confidence:** 93.9% (well-calibrated)
+- **Calibration:** ECE < 0.10
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model
+model_name = "yassine-mhirsi/debertav3-stance-detection"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Predict
+topic = "AI should replace human teachers"
+argument = "Teachers provide emotional support that AI cannot replicate"
+
+text = f"Topic: {{topic}} [SEP] Argument: {{argument}}"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(probs, dim=-1).item()
+
+stance = "PRO" if predicted_class == 1 else "CON"
+confidence = probs[0][predicted_class].item()
+
+print(f"Stance: {{stance}}")
+print(f"Confidence: {{confidence:.2%}}")
+```
+
+## Training Details
+
+- **Epochs:** 3
+- **Learning Rate:** 3e-6
+- **Batch Size:** 4 (with gradient accumulation of 4)
+- **Label Smoothing:** 0.1
+- **Training Time:** ~1.5 hours on Kaggle GPU
+
+## Limitations
+
+- Trained only on English argumentative text
+- Best performance on formal arguments (debate-style)
+- May struggle with heavy sarcasm or irony
+- Calibrated for confidence, but not perfect
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{{stance-detection-deberta,
+  author = Yassine Mhirsi,
+  title = {{Stance Detection with DeBERTa-v3-large}},
+  year = {{2025}},
+  publisher = {{Hugging Face}},
+  howpublished = {{\\url{{https://huggingface.co/yassine-mhirsi/debertav3-stance-detection}}}}
+}}
+```
+
+## License
+
+MIT License
+---