Instructions to use borodache/distilBERT_toxic_detector_multi_label with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use borodache/distilBERT_toxic_detector_multi_label with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="borodache/distilBERT_toxic_detector_multi_label", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("borodache/distilBERT_toxic_detector_multi_label") model = AutoModelForSequenceClassification.from_pretrained("borodache/distilBERT_toxic_detector_multi_label", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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This is a multi label encoder
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A probability of 0.5 or above means this label is True, otherwise it is False. If all labels are False, then the text is toxic free.
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- **Developed by:** Eli Borodach & Yoav Yosef
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This is a multi label encoder SLM. It clasiffies text for NSFW, Hate Speech, and Bullying. The output is a probability between 0 and 1 meaning if that label should be count.
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A probability of 0.5 or above means this label is True, otherwise it is False. If all labels are False, then the text is toxic free.
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- **Developed by:** Eli Borodach & Yoav Yosef
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