Instructions to use Chantland/HRAF_Multilabel_SubClasses with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chantland/HRAF_Multilabel_SubClasses with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Chantland/HRAF_Multilabel_SubClasses")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Chantland/HRAF_Multilabel_SubClasses") model = AutoModelForSequenceClassification.from_pretrained("Chantland/HRAF_Multilabel_SubClasses") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 024a490d9b7308d9473797dd6ae535f0b1c0caa3eb5f67299bd01a9ffa8f7833
- Size of remote file:
- 499 MB
- SHA256:
- e5ca05876cf3afcab6dd3663ec1da3c4140beba4d57538335952e4fcbc5726f8
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