Instructions to use fasherr/toxicity_rubert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fasherr/toxicity_rubert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fasherr/toxicity_rubert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fasherr/toxicity_rubert") model = AutoModelForSequenceClassification.from_pretrained("fasherr/toxicity_rubert") - Notebooks
- Google Colab
- Kaggle
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**Eval results**
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|| Accuracy | Precision | Recall | F1-Score | AUC-ROC | Support |
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| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
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| **Overall
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| **Neutral** |
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| **Toxic** |
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```
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**Eval results**
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| Category | Accuracy | Precision | Recall | F1-Score | AUC-ROC | Support |
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| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
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| **Overall** | 97.93% | 96.37% | 96.86% | 96.61% | 0.9962 | 26271 |
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| **Neutral** | - | 98.88% | 98.57% | 98.72% | - | 21347 |
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| **Toxic** | - | 93.87% | 95.15% | 94.50% | - | 4924 |
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