| --- |
| language: en |
| tags: |
| - emotion-classification |
| - text-classification |
| - deberta |
| license: apache-2.0 |
| --- |
| |
| # DeBERTa Emotion Classifier |
|
|
| This model classifies text into 5 emotions: |
| - 😠 Anger |
| - 😨 Fear |
| - 😊 Joy |
| - 😢 Sadness |
| - 😲 Surprise |
|
|
| ## Usage |
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch |
| |
| model_name = "Somya26/deberta-emotion-classifier" |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) |
| |
| text = "I am so happy today!" |
| inputs = tokenizer(text, return_tensors="pt") |
| outputs = model(**inputs) |
| probs = torch.sigmoid(outputs.logits).squeeze() |
| |
| emotions = ['anger', 'fear', 'joy', 'sadness', 'surprise'] |
| for emotion, prob in zip(emotions, probs): |
| print(f"{emotion}: {prob:.2%}") |
| ``` |
|
|
| ## Training |
|
|
| Trained on emotion classification dataset using DeBERTa-v3-base. |
|
|
