Instructions to use NTCAL/SavedAfterTrainingTest39 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTCAL/SavedAfterTrainingTest39 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NTCAL/SavedAfterTrainingTest39")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NTCAL/SavedAfterTrainingTest39") model = AutoModelForSequenceClassification.from_pretrained("NTCAL/SavedAfterTrainingTest39", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ce8665bcc8d3901316c0a733ac9671bd1363bc7cc669a658ff5208bd9a62e9e
- Size of remote file:
- 498 MB
- SHA256:
- b15a3b8f0afdc59f83c56ff14b2ec6b8c9e4e7819a82dd8f9c70d63e570da113
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