File size: 6,122 Bytes
e148be1 b8830cf e148be1 b8830cf e148be1 b8830cf 3fffb4b e148be1 b8830cf e148be1 b8830cf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
---
license: apache-2.0
tags:
- emotion-detection
- text-classification
- transformers
- deberta
- huggingface
- emotion
- emotion-classification
datasets:
- dair-ai/emotion
- faisalsanto007/isear-dataset
- debarshichanda/goemotions
metrics:
- accuracy
- precision
- recall
- f1
- confusion_matrix
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
- name: F1
type: f1
value: 0.94
base_model:
- microsoft/deberta-v3-large
---
# DeBERTa-v3-Large for Emotion Detection (Merged & Augmented Dataset)
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:
- 🤗 [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 | 0.9569 | 0.9434 | 0.9569 | 0.9444 | 0.9571 | 0.9428 | 0.9569 |
---
## 🛠️ 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
---
## 📊 Classification Report
---
## 🔧 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}
}
``` |