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---
language: en
license: apache-2.0
tags:
- metaphor-detection
- bert
- text-classification
- nlp
- transformer
model-index:
- name: Fine-Tuned Metaphor Detection Model
results:
- task:
name: text-classification
type: text-classification
metrics:
- name: Accuracy
value: 72
type: accuracy
metrics:
- accuracy
base_model:
- Sasidhar1826/common_metaphors_detection
pipeline_tag: text-classification
datasets:
- Sasidhar1826/manual_data_on_metaphors
---
# Fine-Tuned Metaphor Detection Model
This is the extention of my previously trained model.
This is a fine-tuned version of a BERT-based model used for metaphor detection in text. The model 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
- **Evaluation metric**: Accuracy
- **Accuracy**: 72%
## How to use
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:
```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") |