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--- |
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license: apache-2.0 |
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tags: |
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- emotion-detection |
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- text-classification |
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- transformers |
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- deberta |
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- huggingface |
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- emotion |
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- emotion-classification |
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datasets: |
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- dair-ai/emotion |
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- faisalsanto007/isear-dataset |
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- debarshichanda/goemotions |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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- confusion_matrix |
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model-index: |
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- name: Emotion-Classification-DeBERTa-v3-Large |
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results: |
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- task: |
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type: text-classification |
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name: Emotion Classification |
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dataset: |
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name: Merged Emotion Datasets (GoEmotions + ISEAR + Emotion) |
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type: text |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.96 |
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- name: F1 |
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type: f1 |
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value: 0.94 |
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base_model: |
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- microsoft/deberta-v3-large |
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--- |
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# DeBERTa-v3-Large for Emotion Detection (Merged & Augmented Dataset) |
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This model is fine-tuned on [`microsoft/deberta-v3-large`](https://huggingface.co/microsoft/deberta-v3-large) on a **merged and augmented** version of the following datasets: |
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- 🤗 [GoEmotions](https://huggingface.co/datasets/debarshichanda/goemotions) |
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- 📘 [ISEAR Dataset](https://www.kaggle.com/datasets/faisalsanto007/isear-dataset/data) |
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- 📙 [Emotion Dataset (DAIR-AI)](https://huggingface.co/datasets/dair-ai/emotion) |
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The model is trained for **7-class emotion classification** in English and achieves **state-of-the-art performance** using advanced augmentation and weighted loss. |
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--- |
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## 🧠 Emotion Classes |
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- 😠 **anger** |
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- 🤢 **disgust** |
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- 😨 **fear** |
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- 😀 **happy** |
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- 😐 **neutral** |
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- 😢 **sad** |
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- 😲 **surprise** |
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--- |
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## 📈 Training Metrics |
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| Epoch | Training Loss | Validation Loss | Accuracy | F1 Macro | F1 Weighted | Precision Macro | Precision Weighted | Recall Macro | Recall Weighted | |
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| ----- | ------------- | --------------- | -------- | -------- | ----------- | --------------- | ------------------ | ------------ | --------------- | |
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| 1 | 0.3867 | 0.3506 | 0.7559 | 0.6857 | 0.7629 | 0.6520 | 0.7859 | 0.7722 | 0.7559 | |
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| 2 | 0.2340 | 0.2120 | 0.8147 | 0.7879 | 0.8174 | 0.7557 | 0.8292 | 0.8365 | 0.8147 | |
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| 3 | 0.1786 | 0.1616 | 0.8428 | 0.8114 | 0.8445 | 0.7715 | 0.8533 | 0.8758 | 0.8428 | |
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| 4 | 0.1261 | 0.1371 | 0.8671 | 0.8584 | 0.8669 | 0.8479 | 0.8729 | 0.8754 | 0.8671 | |
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| 5 | 0.0770 | 0.1242 | 0.8940 | 0.8751 | 0.8936 | 0.8537 | 0.8965 | 0.9020 | 0.8940 | |
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| 6 | 0.0608 | 0.1190 | 0.9208 | 0.9179 | 0.9221 | 0.9171 | 0.9225 | 0.9195 | 0.9208 | |
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| 7 | 0.0462 | 0.1209 | 0.9255 | 0.9192 | 0.9253 | 0.9218 | 0.9269 | 0.9184 | 0.9255 | |
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| 8 | 0.0373 | 0.1251 | 0.9305 | 0.9198 | 0.9305 | 0.9145 | 0.9317 | 0.9262 | 0.9305 | |
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| 9 | 0.0270 | 0.1262 | 0.9453 | 0.9375 | 0.9453 | 0.9354 | 0.9462 | 0.9400 | 0.9453 | |
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| 10 | 0.0189 | 0.1304 | 0.9526 | 0.9412 | 0.9527 | 0.9408 | 0.9529 | 0.9421 | 0.9526 | |
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| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | |
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| 20 | 0.0025 | 0.1618 | 0.9569 | 0.9434 | 0.9569 | 0.9444 | 0.9571 | 0.9428 | 0.9569 | |
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--- |
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## 🛠️ Training Configuration |
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```python |
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training_args = TrainingArguments( |
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output_dir="./deberta-large-3-merged_augmented", |
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eval_strategy="epoch", |
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save_strategy="epoch", |
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learning_rate=1e-5, |
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per_device_train_batch_size=32, |
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per_device_eval_batch_size=32, |
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gradient_accumulation_steps=2, |
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num_train_epochs=20, |
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weight_decay=0.01, |
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lr_scheduler_type="cosine", |
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logging_dir="./logs", |
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logging_steps=50, |
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save_total_limit=1, |
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load_best_model_at_end=True, |
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metric_for_best_model="accuracy", |
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report_to="none", |
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dataloader_num_workers=8 |
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) |
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``` |
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--- |
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## 🔄 Confusion Matrix |
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--- |
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## 📊 Classification Report |
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--- |
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## 🔧 How to Use |
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```python |
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from transformers import DebertaV2Tokenizer, DebertaV2ForSequenceClassification |
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import torch |
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text = "I'm feeling very nervous about tomorrow." |
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tokenizer = DebertaV2Tokenizer.from_pretrained('Tanneru/Emotion-Classification-DeBERTa-v3-Large') |
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model = DebertaV2ForSequenceClassification.from_pretrained('Tanneru/Emotion-Classification-DeBERTa-v3-Large') |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model(**inputs) |
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predicted_class_id = torch.argmax(outputs.logits).item() |
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print("Predicted emotion:", model.config.id2label[predicted_class_id]) |
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``` |
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--- |
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## 📄 License |
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This model is released under the **Apache 2.0 License**. You are free to use, modify, and distribute the model with proper attribution. |
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--- |
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## ✍️ Author |
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* **Username**: Tanneru |
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* **Base model**: [`microsoft/deberta-v3-large`](https://huggingface.co/microsoft/deberta-v3-large) |
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--- |
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## 📚 Citation |
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If you use this model in your work, please cite: |
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```bibtex |
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@misc{tanneru2025deberta_emotion, |
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title={DeBERTa-v3-Large fine-tuned on Merged & Augmented Emotion Datasets}, |
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author={Tanneru}, |
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year={2025}, |
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publisher={Hugging Face}, |
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howpublished={\url{https://huggingface.co/Tanneru/Emotion-Classification-DeBERTa-v3-Large}}, |
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} |
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@article{he2021deberta, |
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title={DeBERTa: Decoding-enhanced BERT with Disentangled Attention}, |
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author={He, Pengcheng and Liu, Xiaodong and Gao, Jianfeng and Chen, Weizhu}, |
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journal={arXiv preprint arXiv:2006.03654}, |
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year={2021} |
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} |
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``` |
