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zhangxiaoxin/
distilbert-base-uncased-finetuned-emotion

Name: distilbert-base-uncased-finetuned-emotion
Availability: InStock

emotion-classification

sentiment-analysis

emotion

mit

text-embeddings-inference

endpoints_compatible

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Emotion Classification with Fine-tuned DistilBERT

This repository provides a fine-tuned DistilBERT model for multi-class emotion classification. The model is trained on a six-category emotion dataset and achieves strong performance compared to traditional machine learning baselines.

⚠️ Note: This project was developed as part of a course assignment for educational and research purposes.

🔖 Emotion Labels

sadness
joy
love
anger
fear
surprise

🚀 Model Details

Base model: distilbert-base-uncased
Task: Sequence Classification
Number of labels: 6
Framework: Hugging Face Transformers
Training method: Full fine-tuning

📊 Performance (Validation Set)

Accuracy: ~94.3%
F1-score (weighted): ~94.4%

The fine-tuned Transformer significantly outperforms classical models such as Logistic Regression, SVM, Random Forest, and Gradient Boosting trained on frozen hidden-state features.

📦 Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("zhangxiaoxin/distilbert-base-uncased-finetuned-emotion")
model = AutoModelForSequenceClassification.from_pretrained(
    "zhangxiaoxin/distilbert-base-uncased-finetuned-emotion"
)

📜 License

This project is released under the MIT License.