Create README.md

README.md

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+# Fine-Tuned Metaphor Detection Model
+
+This is a fine-tuned version of the model used for metaphor detection in text. It was trained on a custom dataset with sentences labeled as either metaphors or literals.
+
+## Model Details
+- **Model architecture**: BERT-based model
+- **Number of labels**: 2 (Metaphor, Literal)
+- **Training epochs**: 1
+- **Batch size**: 8
+- **Learning rate**: 1e-5
+
+## How to use
+You can use this model to predict whether a sentence contains a metaphor or not.
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("your-username/fine-tuned-metaphor-detection")
+model = AutoModelForSequenceClassification.from_pretrained("your-username/fine-tuned-metaphor-detection")
+
+# Example text
+text = "Time is a thief."
+
+# Tokenize input and get predictions
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    prediction = torch.argmax(logits, dim=-1)
+
+print("Prediction:", "Metaphor" if prediction.item() == 1 else "Literal")