Instructions to use CWrecker/Bert-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CWrecker/Bert-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CWrecker/Bert-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CWrecker/Bert-Classification") model = AutoModelForSequenceClassification.from_pretrained("CWrecker/Bert-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b52ce59fbc4c6b9796d38c5e0463a953b505c74d7cbd6380eae1cb3c3b6cbf50
- Size of remote file:
- 438 MB
- SHA256:
- 35e077171f9334fde19bc80930db7a4ef6279143ef30ffd3ff77c0aac3d3fdc8
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