Text Classification
Transformers
PyTorch
toxic_comment_xlmr
feature-extraction
toxicity
content-moderation
multilingual
multi-label
xlm-roberta
custom_code
Eval Results (legacy)
Instructions to use Deeptanshuu/mill-screen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deeptanshuu/mill-screen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Deeptanshuu/mill-screen", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Deeptanshuu/mill-screen", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Rename to mill-screen: update repo ID and links
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Shorter model card with baseline comparison and speed charts
Verified signatureAdds AUC and throughput comparison against Detoxify multilingual, textdetox XLM-R large and citizenlab mDistilBERT, measured on the test split. Docstring-only Mill rename in the two Python files.9bec9b1
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Bias warning: add multilingual measurements, English is the least affected language
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Document measured identity-term bias
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Publish multilingual toxicity classifier: weights, card, thresholds, config
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