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UnBIAS-classification-bert
newsmediabias/UnBIAS-classification-bert
UnBIAS-classification-bert is a text classification model from newsmediabias. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as openrail.
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.
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- openrail
- Model type
- bert
- Access
- Public
- Created
- Oct 14, 2023
- Updated
- Oct 25, 2023
- SHA
- fedc4ef3
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Model card
From the Hugging Face model README
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.")
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- openrail
- Languages
- en
- Created
- Oct 14, 2023
- Updated
- Oct 25, 2023