## How to Use

Here is an example of how to use this model to get predictions and convert them back to labels:

```python
import tensorflow as tf
from transformers import TFAutoModelForSequenceClassification, AutoTokenizer
import joblib

# Load the model and tokenizer
model = TFAutoModelForSequenceClassification.from_pretrained("NeuEraAI/Stress_Classifier_BERT")
tokenizer = AutoTokenizer.from_pretrained("NeuEraAI/Stress_Classifier_BERT")

# Load your label encoder
label_encoder = joblib.load("label_encoder.joblib")

def decode_predictions(predictions):
    # Extract predicted indices (assuming predictions is a list of dicts with 'label' keys)
    predicted_indices = [int(pred['label'].split('_')[-1]) for pred in predictions]
    # Decode the indices to original labels
    decoded_labels = label_encoder.inverse_transform(predicted_indices)
    return decoded_labels

# Example usage
text = "Your example input text here."
decode_predictions(model.predict(text))

{"license": "unknown"}
transformers tf joblib bert text-classification license:unknown autotrain_compatible endpoints_compatible region:us