| --- |
| license: unknown |
| datasets: |
| - The-Adnan-Syed/Reddit-Stress-Classification |
| pipeline_tag: text-classification |
| --- |
| ## 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)) |
