Instructions to use d4data/en_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use d4data/en_pipeline with spaCy:
!pip install https://huggingface.co/d4data/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() - Notebooks
- Google Colab
- Kaggle
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
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Evaluation results
- NER Precisionself-reported0.664
- NER Recallself-reported0.648
- NER F Scoreself-reported0.602