Fine Tuned Metaphor Detection

🚀 Fine-Tuned Metaphor Detection Model

This fine-tuned BERT-based model is designed for metaphor detection in text, offering an effective solution for text classification tasks.

🚀 Quick Start

You can use this model to predict whether a sentence contains a metaphor or not. Below is an example of how to load the model and use it for inference:

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")

✨ Features

• This is the extention of the previously trained model.
• It is a fine - tuned version of a BERT - based model for metaphor detection in text.
• The model was trained on a custom dataset with sentences labeled as either metaphors or literals.

📚 Documentation

Model Details

📄 License

This project is licensed under the Apache-2.0 license.