| --- |
| license: openrail |
| --- |
| ``` |
| |
| import torch |
| from transformers import AutoTokenizer, MobileBertForSequenceClassification |
| |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
| |
| # Load the saved model |
| model_name = 'harshith20/Emotion_predictor' |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = MobileBertForSequenceClassification.from_pretrained(model_name) |
| |
| # Tokenize input text |
| input_text = "I am feeling happy today" |
| input_ids = tokenizer.encode(input_text, add_special_tokens=True, truncation=True, max_length=128) |
| input_tensor = torch.tensor([input_ids]).to(device) |
| |
| |
| # Predict emotion |
| with torch.no_grad(): |
| outputs = model(input_tensor) |
| logits = outputs[0] |
| |
| # Get the predicted label |
| |
| predicted_emotion = torch.argmax(logits, dim=1).item() |
| emotion_labels = {0:'sadness',1:'joy',2:'love',3:'anger',4:'fear',5:'surprise'} |
| predicted_emotion_label = emotion_labels[predicted_emotion] |
| |
| print(f"Input text: {input_text}") |
| print(f"Predicted emotion: {predicted_emotion_label}")``` |
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