Instructions to use khailai/roberta-offensive-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khailai/roberta-offensive-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="khailai/roberta-offensive-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("khailai/roberta-offensive-classifier") model = AutoModelForSequenceClassification.from_pretrained("khailai/roberta-offensive-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Trained on new data DICA_Dec16 (lower-cased and case-preserved) for 5 epochs each. 96% accuracy and 0.88 F1 score
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Trained on new data DICA_Dec15 for 8 epochs. 96% accuracy and 0.85 F1 score
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Trained on training set of DICA_Dec13 (lower-cased) for 3 epochs. Retrained on original DICA_Dec13 (casing-preserved) for 3 epochs.
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Trained on training set of DICA_Dec8 for 2 epochs
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saved labels and id mappings to configs
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initial commit
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