Instructions to use E-MIMIC/inclusively-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use E-MIMIC/inclusively-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="E-MIMIC/inclusively-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("E-MIMIC/inclusively-classification") model = AutoModelForSequenceClassification.from_pretrained("E-MIMIC/inclusively-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c1d972b7e8d596fd643ea542da641bf11c94e7c4cb57c275cb84fdc62dd848eb
- Size of remote file:
- 443 MB
- SHA256:
- f11f5dfedbc3c7bbb7ca7e3eea0fa23fa32cde1b349449bcbe683cf3dd606874
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