Tanneru
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Emotion-Classification-DeBERTa-v3-Large

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+---
+license: apache-2.0
+tags:
+  - emotion-detection
+  - text-classification
+  - transformers
+  - deberta
+  - huggingface
+datasets:
+  - dair-ai/emotion
+  - faisalsanto007/isear-dataset
+  - debarshichanda/goemotions
+metrics:
+  - accuracy
+  - precision
+  - recall
+  - f1
+model-index:
+  - name: Emotion-Classification-DeBERTa-v3-Large
+    results:
+      - task:
+          type: text-classification
+          name: Emotion Classification
+        dataset:
+          name: Merged Emotion Datasets (GoEmotions + ISEAR + Emotion)
+          type: text
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 0.96 # update to your real metric
+          - name: F1
+            type: f1
+            value: 0.94
+---
+---
+# DeBERTa-v3-Large for Emotion Detection (Merged & Augmented Dataset)
+This model fine-tunes [`microsoft/deberta-v3-large`](https://huggingface.co/microsoft/deberta-v3-large) on a **merged and augmented** version of the following datasets:
+- 🤗 [GoEmotions](https://huggingface.co/datasets/debarshichanda/goemotions)
+- 📘 [ISEAR Dataset](https://www.kaggle.com/datasets/faisalsanto007/isear-dataset/data)
+- 📙 [Emotion Dataset (DAIR-AI)](https://huggingface.co/datasets/dair-ai/emotion)
+The model is trained for **7-class emotion classification** in English and achieves **state-of-the-art performance** using advanced augmentation and weighted loss.
+---
+## 🧠 Emotion Classes
+- 😠 **anger**
+- 🤢 **disgust**
+- 😨 **fear**
+- 😀 **happy**
+- 😐 **neutral**
+- 😢 **sad**
+- 😲 **surprise**
+---
+## 📈 Training Metrics
+| Epoch | Training Loss | Validation Loss | Accuracy | F1 Macro | F1 Weighted | Precision Macro | Precision Weighted | Recall Macro | Recall Weighted |
+| ----- | ------------- | --------------- | -------- | -------- | ----------- | --------------- | ------------------ | ------------ | --------------- |
+| 1     | 0.3867        | 0.3506          | 0.7559   | 0.6857   | 0.7629      | 0.6520          | 0.7859             | 0.7722       | 0.7559          |
+| 2     | 0.2340        | 0.2120          | 0.8147   | 0.7879   | 0.8174      | 0.7557          | 0.8292             | 0.8365       | 0.8147          |
+| 3     | 0.1786        | 0.1616          | 0.8428   | 0.8114   | 0.8445      | 0.7715          | 0.8533             | 0.8758       | 0.8428          |
+| 4     | 0.1261        | 0.1371          | 0.8671   | 0.8584   | 0.8669      | 0.8479          | 0.8729             | 0.8754       | 0.8671          |
+| 5     | 0.0770        | 0.1242          | 0.8940   | 0.8751   | 0.8936      | 0.8537          | 0.8965             | 0.9020       | 0.8940          |
+| 6     | 0.0608        | 0.1190          | 0.9208   | 0.9179   | 0.9221      | 0.9171          | 0.9225             | 0.9195       | 0.9208          |
+| 7     | 0.0462        | 0.1209          | 0.9255   | 0.9192   | 0.9253      | 0.9218          | 0.9269             | 0.9184       | 0.9255          |
+| 8     | 0.0373        | 0.1251          | 0.9305   | 0.9198   | 0.9305      | 0.9145          | 0.9317             | 0.9262       | 0.9305          |
+| 9     | 0.0270        | 0.1262          | 0.9453   | 0.9375   | 0.9453      | 0.9354          | 0.9462             | 0.9400       | 0.9453          |
+| 10    | 0.0189        | 0.1304          | 0.9526   | 0.9412   | 0.9527      | 0.9408          | 0.9529             | 0.9421       | 0.9526          |
+| ...   | ...           | ...             | ...      | ...      | ...         | ...             | ...                | ...          | ...             |
+| 20    | 0.0025        | 0.1618          | 0.9572   | 0.9435   | 0.9569      | 0.9444          | 0.9571             | 0.9430       | 0.9572          |
+📆 **Final Accuracy:** **95.72%**
+📆 **Final F1 (Weighted):** **0.957**
+📆 **Final Precision:** **0.944**
+📆 **Final Recall:** **0.943**
+---
+## 🛠️ Training Configuration
+```python
+training_args = TrainingArguments(
+    output_dir="./deberta-large-3-merged_augmented",
+    eval_strategy="epoch",
+    save_strategy="epoch",
+    learning_rate=1e-5,
+    per_device_train_batch_size=32,
+    per_device_eval_batch_size=32,
+    gradient_accumulation_steps=2,
+    num_train_epochs=20,
+    weight_decay=0.01,
+    lr_scheduler_type="cosine",
+    logging_dir="./logs",
+    logging_steps=50,
+    save_total_limit=1,
+    load_best_model_at_end=True,
+    metric_for_best_model="accuracy",
+    report_to="none",
+    dataloader_num_workers=8
+)
+```
+---
+## 🔄 Confusion Matrix
+![Confusion Matrix](https://huggingface.co/Tanneru/Emotion-Classification-DeBERTa-v3-Large/resolve/main/Confusion_Matrix.png)
+---
+## 📊 Classification Report
+![Classification Report](https://huggingface.co/Tanneru/Emotion-Classification-DeBERTa-v3-Large/resolve/main/Classification_Report.png)
+---
+## 🔧 How to Use
+```python
+from transformers import DebertaV2Tokenizer, DebertaV2ForSequenceClassification
+import torch
+text = "I'm feeling very nervous about tomorrow."
+tokenizer = DebertaV2Tokenizer.from_pretrained('Tanneru/Emotion-Classification-DeBERTa-v3-Large')
+model = DebertaV2ForSequenceClassification.from_pretrained('Tanneru/Emotion-Classification-DeBERTa-v3-Large')
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+predicted_class_id = torch.argmax(outputs.logits).item()
+print("Predicted emotion:", model.config.id2label[predicted_class_id])
+```
+---
+## 📄 License
+This model is released under the **Apache 2.0 License**. You are free to use, modify, and distribute the model with proper attribution.
+---
+## ✍️ Author
+* **Username**: Tanneru
+* **Base model**: [`microsoft/deberta-v3-large`](https://huggingface.co/microsoft/deberta-v3-large)
+---
+## 📚 Citation
+If you use this model in your work, please cite:
+```bibtex
+@misc{tanneru2025deberta_emotion,
+  title={DeBERTa-v3-Large fine-tuned on Merged & Augmented Emotion Datasets},
+  author={Tanneru},
+  year={2025},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/Tanneru/Emotion-Classification-DeBERTa-v3-Large}},
+}
+@article{he2021deberta,
+  title={DeBERTa: Decoding-enhanced BERT with Disentangled Attention},
+  author={He, Pengcheng and Liu, Xiaodong and Gao, Jianfeng and Chen, Weizhu},
+  journal={arXiv preprint arXiv:2006.03654},
+  year={2021}
+}
+```