Instructions to use shawhin/bert-phishing-classifier_student with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shawhin/bert-phishing-classifier_student with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shawhin/bert-phishing-classifier_student")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shawhin/bert-phishing-classifier_student") model = AutoModelForSequenceClassification.from_pretrained("shawhin/bert-phishing-classifier_student") - Notebooks
- Google Colab
- Kaggle
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Student model for knowledge distillation example.
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Video
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Blog: https://towardsdatascience.com/compressing-large-language-models-llms-9f406eea5b5e <br>
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Example code: https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/model-compression
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## Intended uses & limitations
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Student model for knowledge distillation example.
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[Video](https://youtu.be/FLkUOkeMd5M) | [Blog](https://towardsdatascience.com/compressing-large-language-models-llms-9f406eea5b5e) | [Example code](https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/model-compression)
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## Intended uses & limitations
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