Instructions to use TimSchopf/nlp_taxonomy_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TimSchopf/nlp_taxonomy_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TimSchopf/nlp_taxonomy_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TimSchopf/nlp_taxonomy_classifier") model = AutoModelForSequenceClassification.from_pretrained("TimSchopf/nlp_taxonomy_classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46a3d7cfcc4795bebb23d7828f0893d644f4421ba9a9532d28a61dd61c0eceb5
- Size of remote file:
- 440 MB
- SHA256:
- 26c76d1d4cec3d18a0290a5de526e7a6128fe102a5863e45adf107a087526780
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