Mango-Juice/trpg_emotion_classification

GoEmotions Fine-tuned Model

This is a multi-label emotion classification model trained on the GoEmotions dataset and TRPG sentences.

Model Information

• Base Model: Mango-Juice/trpg_mlm
• Task: Multi-label Emotion Classification
• Labels: 28 emotion labels
• Training: Completed a two-stage fine-tuning process (1st stage: GoEmotions data, 2nd stage: TRPG sentence data)

Emotion Labels

• admiration
• amusement
• anger
• annoyance
• approval
• caring
• confusion
• curiosity
• desire
• disappointment
• disapproval
• disgust
• embarrassment
• excitement
• fear
• gratitude
• grief
• joy
• love
• nervousness
• optimism
• pride
• realization
• relief
• remorse
• sadness
• surprise
• neutral

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Mango-Juice/trpg_emotion_classification")
model = AutoModelForSequenceClassification.from_pretrained("Mango-Juice/trpg_emotion_classification")

# Inference
def predict_emotions(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
    with torch.no_grad():
        logits = model(**inputs).logits
        probs = torch.sigmoid(logits).cpu().numpy()[0]

    emotion_labels = ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral']
    return {emotion: float(prob) for emotion, prob in zip(emotion_labels, probs)}

# Example
text = "I am so happy today!"
emotions = predict_emotions(text)
print(emotions)

Performance

• The fine-tuned model provides improved performance in emotion classification.
• Data augmentation was applied for minority classes.

Training Details

• Data Augmentation: Oversampling based on paraphrasing and back-translation.
• Loss Function: Focal Loss with Label Smoothing
• Optimizer: AdamW
• Scheduler: ReduceLROnPlateau