metadata
license: mit
datasets:
- mrm8488/goemotions
- IconicAI/DDD
language:
- en
metrics:
- accuracy
- f1
base_model:
- Mango-Juice/trpg_mlm
- microsoft/deberta-v3-large
library_name: transformers
model-index:
- name: trpg_emotion_classification
results:
- task:
type: text-classification
dataset:
name: IconicAI/DDD (custom subset manually labeled)
type: custom
split: test
config: csv
metrics:
- type: accuracy
value: 0.929
- type: f1
value: 0.476
name: f1 macro
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