| |
| import torch |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
|
|
| class MetaphorScorer: |
| def __init__(self, model_path='.'): |
| """ |
| Initialize the metaphor scorer. |
| |
| Args: |
| model_path: Path or Hugging Face repo ID. |
| Default '.' uses current directory (where model files are) |
| Or use 'your-username/Metaphor_Scoring_Model' to load from Hub |
| """ |
| |
| self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
| |
| print(f"Loading model from: {model_path}") |
| self.tokenizer = AutoTokenizer.from_pretrained(model_path) |
| self.model = AutoModelForSequenceClassification.from_pretrained(model_path) |
| self.model.to(self.device) |
| self.model.eval() |
| print(f"Model loaded on {self.device}") |
| |
| def score_sentence(self, sentence): |
| """ |
| Score a sentence for metaphorical novelty. |
| |
| Args: |
| sentence: Input sentence to score |
| |
| Returns: |
| score: Novelty score (1-4) |
| confidence: Model confidence (0-1) |
| """ |
| inputs = self.tokenizer( |
| sentence, |
| return_tensors='pt', |
| max_length=256, |
| truncation=True, |
| padding='max_length' |
| ) |
| |
| inputs = {k: v.to(self.device) for k, v in inputs.items()} |
| |
| with torch.no_grad(): |
| outputs = self.model(**inputs) |
| logits = outputs.logits |
| predicted_class = torch.argmax(logits, dim=-1).item() |
| score = predicted_class + 1 |
| probabilities = torch.softmax(logits, dim=-1) |
| confidence = probabilities[0][predicted_class].item() |
| |
| return score, confidence |
|
|
| def main(): |
| |
| scorer = MetaphorScorer() |
| |
| print("\n=== Metaphorical Sentence Scorer ===") |
| print("Enter metaphorical sentences to get novelty scores (1-4)") |
| print("Higher scores = Higher metaphorical novelty") |
| print("Type 'quit' to exit\n") |
| |
| while True: |
| sentence = input("Enter sentence: ").strip() |
| |
| if sentence.lower() in ['quit', 'exit', 'q']: |
| print("Goodbye!") |
| break |
| |
| if not sentence: |
| continue |
| |
| score, confidence = scorer.score_sentence(sentence) |
| print(f"Score: {score}/4 (confidence: {confidence:.3f})\n") |
|
|
| if __name__ == "__main__": |
| main() |
|
|
