---
tags:
- spacy
- token-classification
co2_eq_emissions: Kg
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: "There have been a protest by a group of people"
  example_title: "Non-Biased example 1"
- text: "While emphasizing he’s not singling out either party, Cohen warned about the danger of normalizing white supremacist ideology."
  example_title: "Non-Biased example 2" 
model-index:
- name: en_bias
  results:
  - task:
      name: NER
      type: token-classification
    metrics:
    - name: NER Precision
      type: precision
      value: 1.0
    - name: NER Recall
      type: recall
      value: 1.0
    - name: NER F Score
      type: f_score
      value: 1.0
---
## 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.

| Feature | Description |
| --- | --- |
| **Name** | `bias` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |


## Usage
The easiest way is to load through the pipeline object offered by transformers library.
```python
!pip install https://huggingface.co/dreji18/en_pipeline/resolve/main/en_pipeline-any-py3-none-any.whl

# Using spacy.load().
import spacy
nlp = spacy.load("en_pipeline")

# Importing as module.
import en_pipeline
nlp = en_pipeline.load()
---

## 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 cite as:
> Bias & Fairness in AI, (2020), GitHub repository, <https://github.com/dreji18/Fairness-in-AI/tree/dev>