Instructions to use muvva/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muvva/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="muvva/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("muvva/results") model = AutoModelForSequenceClassification.from_pretrained("muvva/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e6fe77696f367887860989f80fb8b56e4d4d9a7502d561c069a3c3e54759c84
- Size of remote file:
- 4.86 kB
- SHA256:
- a8a96c1a1a83f2d1568b419791f782f482b68a84b38e7be6ccb6136f434b7439
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