Instructions to use Sk1306/student_chat_toxicity_classifier_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sk1306/student_chat_toxicity_classifier_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sk1306/student_chat_toxicity_classifier_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sk1306/student_chat_toxicity_classifier_model") model = AutoModelForSequenceClassification.from_pretrained("Sk1306/student_chat_toxicity_classifier_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Student Chat Toxicity Classifier
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This model is a fine-tuned version of the `s-nlp/roberta_toxicity_classifier` and is designed to classify text-based messages in student conversations as **toxic** or **non-toxic**. It is specifically tailored to detect and flag malpractice suggestions, unethical advice, or any toxic communication while encouraging ethical and positive interactions among students.
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pipeline_tag: text-classification
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library_name: transformers
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### Student Chat Toxicity Classifier
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This model is a fine-tuned version of the `s-nlp/roberta_toxicity_classifier` and is designed to classify text-based messages in student conversations as **toxic** or **non-toxic**. It is specifically tailored to detect and flag malpractice suggestions, unethical advice, or any toxic communication while encouraging ethical and positive interactions among students.
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