Instructions to use CWrecker/DistilBert-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CWrecker/DistilBert-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CWrecker/DistilBert-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CWrecker/DistilBert-Classification") model = AutoModelForSequenceClassification.from_pretrained("CWrecker/DistilBert-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb5b8555cac8d2d988e5ec63daa6235ac3a113a017e2f1264bde6d580354cb5d
- Size of remote file:
- 268 MB
- SHA256:
- 95ec8998cea48b23c266e805b4144719f4860046d831e5711d988dcc0aeeaa98
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