Instructions to use d4data/bias-detection-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d4data/bias-detection-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="d4data/bias-detection-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("d4data/bias-detection-model") model = AutoModelForSequenceClassification.from_pretrained("d4data/bias-detection-model") - Notebooks
- Google Colab
- Kaggle
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## About the Model
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An English sequence classification model, trained on MBAD Dataset to detect bias and fairness in sentences. This model was built on top of distilbert-base-uncased model and trained for 30 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512.
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- Dataset : MBAD Data
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- Carbon emission 0.319355 Kg
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## About the Model
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An English sequence classification model, trained on MBAD Dataset to detect bias and fairness in sentences (news articles). This model was built on top of distilbert-base-uncased model and trained for 30 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512.
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- Dataset : MBAD Data
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- Carbon emission 0.319355 Kg
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