from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np

# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("hrshlgunjal/emotion-classifier-deberta-v3")
tokenizer = AutoTokenizer.from_pretrained("hrshlgunjal/emotion-classifier-deberta-v3")

# Optimized thresholds (use these for best results)
thresholds = np.array([0.5, 0.5, 0.5, 0.5, 0.5])
labels = ['anger', 'fear', 'joy', 'sadness', 'surprise']

# Predict emotions
def predict_emotions(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
    with torch.no_grad():
        outputs = model(**inputs)
    probs = torch.sigmoid(outputs.logits).cpu().numpy()[0]
    predictions = (probs >= thresholds).astype(int)
    return {label: (pred, prob) for label, pred, prob in zip(labels, predictions, probs)}

# Example
text = "I am so excited about this amazing opportunity!"
result = predict_emotions(text)
print(result)

Training Details

Optimizer: AdamW with differential weight decay
Learning Rate: 1.5e-05
Batch Size: 16
Epochs: 4
Max Sequence Length: 128
Warmup Ratio: 0.1
Weight Decay: 0.01
Mixed Precision: Enabled (FP16)
Gradient Clipping: 1.0

Training Infrastructure

Device: GPU
Training Time: ~300 minutes (approximate)
Framework Versions:
- PyTorch: 2.6.0+cu124
- Transformers: 4.53.3

Model Card Authors

hrshlgunjal

Model Card Contact

For questions or feedback, please open an issue in the model repository.