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Update app.py
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app.py
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@@ -146,16 +146,19 @@ with iface:
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with gr.Row():
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gr.Markdown(
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"""
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#
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- **Generalizations (GEN)**
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- **Unfairness (UNFAIR)**
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- **Stereotypes (STEREO)**
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Labels follow the BIO format. Try it out
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- **[Blog Post](https://huggingface.co/blog/maximuspowers/bias-entity-recognition)**
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- **[Model Page](https://huggingface.co/maximuspowers/bias-detection-ner)**
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with gr.Row():
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gr.Markdown(
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# GUS-Net 🕵
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[GUS-Net](https://huggingface.co/ethical-spectacle/social-bias-ner) is a `BertForTokenClassification` based model, trained on the [GUS dataset](https://huggingface.co/datasets/ethical-spectacle/gus-dataset-v1). It preforms multi-label named-entity recognition of socially biased entities, intended to reveal the underlying structure of bias rather than a one-size fits all definition.
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You can find the full collection of resources introduced in our paper [here](https://huggingface.co/collections/ethical-spectacle/gus-net-66edfe93801ea45d7a26a10f).
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This [blog post](https://huggingface.co/blog/maximuspowers/bias-entity-recognition) walks through the training and architecture of the model.
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Enter a sentence for named-entity recognition of biased entities:
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- **Generalizations (GEN)**
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- **Unfairness (UNFAIR)**
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- **Stereotypes (STEREO)**
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Labels follow the BIO format. Try it out:
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"""
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with gr.Row():
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