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--- |
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tags: |
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- spacy |
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- token-classification |
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co2_eq_emissions: Kg |
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language: |
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- en |
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widget: |
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- text: "Billie Eilish issues apology for mouthing an anti-Asian derogatory term in a resurfaced video." |
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example_title: "Biased example 1" |
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- text: "Christians should make clear that the perpetuation of objectionable vaccines and the lack of alternatives is a kind of coercion." |
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example_title: "Biased example 2" |
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- text: "There have been a protest by a group of people" |
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example_title: "Non-Biased example 1" |
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- text: "While emphasizing he’s not singling out either party, Cohen warned about the danger of normalizing white supremacist ideology." |
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example_title: "Non-Biased example 2" |
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model-index: |
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- name: en_bias |
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results: |
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- task: |
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name: NER |
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type: token-classification |
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metrics: |
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- name: NER Precision |
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type: precision |
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value: 1.0 |
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- name: NER Recall |
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type: recall |
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value: 1.0 |
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- name: NER F Score |
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type: f_score |
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value: 1.0 |
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--- |
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## About the Model |
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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. |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `bias` | |
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| **Version** | `0.0.0` | |
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| **spaCy** | `>=3.2.1,<3.3.0` | |
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| **Default Pipeline** | `transformer`, `ner` | |
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| **Components** | `transformer`, `ner` | |
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## Usage |
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The easiest way is to load through the pipeline object offered by transformers library. |
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```python |
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!pip install https://huggingface.co/dreji18/en_pipeline/resolve/main/en_pipeline-any-py3-none-any.whl |
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# Using spacy.load(). |
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import spacy |
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nlp = spacy.load("en_pipeline") |
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# Importing as module. |
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import en_pipeline |
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nlp = en_pipeline.load() |
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--- |
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## Author |
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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: |
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> Bias & Fairness in AI, (2020), GitHub repository, <https://github.com/dreji18/Fairness-in-AI/tree/dev> |
