| | --- |
| | license: apache-2.0 |
| | tags: |
| | - text-classification |
| | - emotion |
| | - NLP |
| | - DeBERTa |
| | language: en |
| | datasets: |
| | - GoEmotions |
| | metrics: |
| | - Training Accuracy |
| | - Validation Accuracy |
| | - Testing Accuracy |
| | - Precisionm |
| | - Recall |
| | - F-1(micro) |
| | pipeline_tag: text-classification |
| | --- |
| | # Emotion DeBERTa – 5-Class Emotion Classifier |
| |
|
| | ## Model Description |
| |
|
| | # Emotion DeBERTa – 5-Class Emotion Classifier |
| |
|
| | ## Model Description |
| |
|
| | This model is a fine-tuned version of **DeBERTa-v3-base** for emotion classification. |
| | It predicts one of five emotional states from input text: |
| |
|
| | - `anger` |
| | - `fear` |
| | - `joy` |
| | - `sadness` |
| | - `surprise` |
| |
|
| | The model was trained as part of a university capstone project focused on building an emotion-aware mental healthcare companion. |
| |
|
| | ## Base Model |
| |
|
| | - `microsoft/deberta-v3-base` |
| |
|
| | ## Training Details |
| |
|
| | - **Task:** Text-based emotion classification |
| | - **Architecture:** DeBERTa encoder with a custom classification head |
| | - **Number of labels:** 5 |
| | - **Training method:** Supervised fine-tuning |
| | - **Output:** Single-label emotion prediction |
| |
|
| | The model was originally trained using a custom PyTorch class and later converted into Hugging Face format for deployment and reproducibility. |
| |
|
| | ## Intended Use |
| |
|
| | This model is designed for: |
| |
|
| | - Emotion-aware chat applications |
| | - Mental health companion systems |
| | - Sentiment and emotional analysis in academic projects |
| | - Research and educational purposes |
| |
|
| | It is **not** intended for clinical diagnosis or professional mental health decisions. |
| |
|
| | ## Limitations |
| |
|
| | - Trained on a limited dataset |
| | - May not generalize well to: |
| | - Slang-heavy text |
| | - Code-mixed or multilingual inputs |
| | - Highly sarcastic or ambiguous sentences |
| | - Predictions should be treated as probabilistic, not factual |
| |
|
| | ## Example Usage |
| |
|
| | ```python |
| | from transformers import pipeline |
| | |
| | classifier = pipeline( |
| | task="text-classification", |
| | model="Sadman4701/Apricity-Final", |
| | return_all_scores=True |
| | ) |
| | |
| | text = "I feel scared but also strangely hopeful about the future." |
| | |
| | outputs = classifier(text) |
| | |
| | THRESHOLD = 0.5 #change it according to your preferences |
| | predicted_emotions = [ |
| | o["label"] for o in outputs[0] if o["score"] >= THRESHOLD |
| | ] |
| | |
| | print(predicted_emotions) |
