---
pipeline_tag: text-classification
tags:
- code
license: openrail
language:
- en
library_name: transformers
---
# Model Details
Classifier-Bias-TahniatKhan is a prototype model crafted to classify content into two categories: "Biased" and "Non-Biased".

# Model Architecture
The model is built upon the distilbert-base-uncased architecture and has been fine-tuned on a custom dataset for the specific task of bias detection.

# Dataset
The model was trained on a BABE dataset containing news articles from various sources, annotated with one of the 2 bias levels.
Biased_Text = 1810
UnBiased_Test=1810

# Training Procedure
The model was trained using the Adam optimizer for 6 epochs.

Performance
On our validation set, the model achieved:

Accuracy: 78%
F1 Score (Biased): 79%
F1 Score (Non-Biased): 78%

# How to Use
To use this model for text classification, use the following code
```python
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
tokenizer = AutoTokenizer.from_pretrained("/tahniat/Classifier_bias_TahniatKhan")
model = AutoModelForSequenceClassification.from_pretrained("/tahniat/Classifier_bias_TahniatKhan")

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = classifier("Men are better drivers")
print(result)
```

# Caveats and Limitations
The model's training data originates from a specific dataset (BABE) which might not represent all kinds of biases or content.

The performance metrics are based on a random validation split, so the model's performance might vary in real-world applications.