🔥 Fine-Tuned BERT on GoEmotions Dataset

📖 Model Overview

This model is a fine-tuned version of BERT (bert-base-uncased) on the GoEmotions dataset for multi-label emotion classification. It can predict multiple emotions per input text.

📊 Performance

Metric	Score
Accuracy	46.57%
F1 Score	56.41%
Hamming Loss	3.39%

📂 Model Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "codewithdark/bert-Gomotions"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Emotion labels (adjust based on your dataset)
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"
]

# Example text
text = "I'm so happy today!"
inputs = tokenizer(text, return_tensors="pt")

# Predict
with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.sigmoid(outputs.logits).squeeze(0)  # Convert logits to probabilities

# Get top 5 predictions
top5_indices = torch.argsort(probs, descending=True)[:5]  # Get indices of top 5 labels
top5_labels = [emotion_labels[i] for i in top5_indices]
top5_probs = [probs[i].item() for i in top5_indices]

# Print results
print("Top 5 Predicted Emotions:")
for label, prob in zip(top5_labels, top5_probs):
    print(f"{label}: {prob:.4f}")

'''
output:
Top 5 Predicted Emotions:
Joy: 0.9478
Love: 0.7854
Optimism: 0.6342
Admiration: 0.5678
Excitement: 0.5231
'''

🏋️‍♂️ Training Details

Model: bert-base-uncased
Dataset: GoEmotions
Optimizer: AdamW
Loss Function: BCEWithLogitsLoss (Binary Cross-Entropy for multi-label classification)
Batch Size: 16
Epochs: 3
Evaluation Metrics: Accuracy, F1 Score, Hamming Loss

📌 How to Use in Hugging Face

from transformers import pipeline

classifier = pipeline("text-classification", model="codewithdark/bert-Gomotions", top_k=None)
classifier("I'm so excited about the trip!")

🛠️ Citation

If you use this model, please cite:

@misc{your_model,
  author = {codewithdark},
  title = {Fine-tuned BERT on GoEmotions},
  year = {2025},
  url = {https://huggingface.co/codewithdark/bert-Gomotions}
}

codewithdark
/

bert-Gomotions