Instructions to use ElMad/capricious-gnu-139 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ElMad/capricious-gnu-139 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ElMad/capricious-gnu-139")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ElMad/capricious-gnu-139") model = AutoModelForSequenceClassification.from_pretrained("ElMad/capricious-gnu-139") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f044ca06d7169c45cc5b332ea41dad1e04464c918d2bb2a564b4b54c0e8122cb
- Size of remote file:
- 5.3 kB
- SHA256:
- c52c531dc6391254ac1cf77608fc39f3252795204e81d6e057a5b2d850e8ed4a
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