Instructions to use darkzara/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use darkzara/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="darkzara/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("darkzara/results") model = AutoModelForSequenceClassification.from_pretrained("darkzara/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c01ba65eccb744add7680d5a91af640d2dfe829dd984519d8584f1f6d231feaa
- Size of remote file:
- 2.93 kB
- SHA256:
- 6d6d06d4163c7660724ef06812283018fdbdd60ad1a3afc4d087d6ef0c3a7948
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