Instructions to use himel7/da-multilingual-bias-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use himel7/da-multilingual-bias-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="himel7/da-multilingual-bias-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("himel7/da-multilingual-bias-detector") model = AutoModelForSequenceClassification.from_pretrained("himel7/da-multilingual-bias-detector") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
This is a model for bias detection in news texts in multiple languages.
This model is distilled from a pre-trained domain adapted and fine tuned bias detection model, hence it transfers the domain-adapted learning approach along with the fine-tuning to a multilingual xlm-roberta base model.
On BABE Test Set in English: Accuracy- 0.826 Macro F1- 0.8324
On BABE Test Set in Hindi: Accuracy- 0.798 Macro F1- 0.8206
On BABE Test Set in Bengali: Accuracy- 0.757 Macro F1- 0.797
You can add further metrics in other languages here...
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
Developed by: Himel Ghosh
Language(s) (NLP): English, Italian, German, etc.
Finetuned from model xlm-roberta: Distilled from mediabiasgroup/da-roberta-babe-ft
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Model tree for himel7/da-multilingual-bias-detector
Base model
FacebookAI/xlm-roberta-base