---
license: openrail
language:
- en
datasets:
- newsmediabias/news-bias-full-data
---

## Bias Classification Using Bert

# Overview:

This is a BERT based model designed to detect bias in text data enabling users to identify whether a given text is biased or non-biased.



## Performance:

The model's performance on unseen data is:

### Non-biased	Precision: 0.93	   Recall: 0.96

### Biased	Precision: 0.91	Recall: 0.88

## Overall accuracy : 0.93



## Usage

To use the model, you can utilize the transformers library from Hugging Face:

```
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("newsmediabias/UnBIAS-classification-bert")
model = AutoModelForSequenceClassification.from_pretrained("newsmediabias/UnBIAS-classification-bert")

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer , device=0 if device.type == "cuda" else -1)


classifier("Anyone can excel at coding.")
```


![image/png](https://cdn-uploads.huggingface.co/production/uploads/64fb380148411fc78972acab/eENc3vE327tVweJ8zWICb.png)