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:
- 226a881b2714c2d1996dd677f6bdf4a5300ebc47eba682461ad21926657aadd4
- Size of remote file:
- 438 MB
- SHA256:
- 3eeb6db42698e5dc8c4bfae67eadbadda04398507d093c37d1126dab7a2cd7f2
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