Instructions to use gportillac/Case_03_Natural_Language_Processing_Class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gportillac/Case_03_Natural_Language_Processing_Class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gportillac/Case_03_Natural_Language_Processing_Class")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gportillac/Case_03_Natural_Language_Processing_Class") model = AutoModelForSequenceClassification.from_pretrained("gportillac/Case_03_Natural_Language_Processing_Class") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4f2e76baeddeb75d0d4a15cb8a5372fb0767e960b942d8ffb11c1d9b0e9c07f
- Size of remote file:
- 438 MB
- SHA256:
- 3506b69075ed7f3df02384b34be4a49ce9ff33299d97a9df8a00a63bf323a6c1
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