Instructions to use gelabgaboo/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gelabgaboo/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gelabgaboo/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gelabgaboo/results") model = AutoModelForSequenceClassification.from_pretrained("gelabgaboo/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4ef4dc803f745c0cf580b613a7db5898dd563d64953d2c6ff42ad7337403048
- Size of remote file:
- 1.68 GB
- SHA256:
- e77ee4632988b967ad4138f0227ae32e0a1c3816c789f66b3bc91dfc08879c91
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