metadata
tags:
- spacy
- token-classification
language:
- en
widget:
- text: >-
Billie Eilish issues apology for mouthing an anti-Asian derogatory term in
a resurfaced video.
example_title: Biased example 1
- text: >-
Christians should make clear that the perpetuation of objectionable
vaccines and the lack of alternatives is a kind of coercion.
example_title: Biased example 2
- text: >-
But, whether this switch constitutes a true win for the racist right or
not, it’s clear that MAGA conservatives are highly attuned to how
decisions are made in the White House and which positions they want to
control.
example_title: Biased example 3
- text: >-
The fact that the abortion rate among American blacks is far higher than
the rate for whites is routinely chronicled and mourned.
example_title: Biased example 4
model-index:
- name: en_bias
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.6642
- name: NER Recall
type: recall
value: 0.6485
- name: NER F Score
type: f_score
value: 0.6022
About the Model
This model is trained on MBAD Dataset to recognize the biased word/phrases in a sentence. This model was built on top of roberta-base offered by Spacy transformers.
This model is in association with https://huggingface.co/d4data/bias-detection-model
| Feature | Description |
|---|---|
| Name | Bias Recognizer Model |
| Version | 1.0 |
| spaCy | >=3.2.1,<3.3.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
Author
This model is part of the Research topic "Bias and Fairness in AI" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at:
Bias & Fairness in AI, (2022), GitHub repository, https://github.com/dreji18/Fairness-in-AI