Instructions to use Patcas/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Patcas/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Patcas/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Patcas/results") model = AutoModelForSequenceClassification.from_pretrained("Patcas/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6b96b629a851b3be63613154a518191bb3f845c07586f9393f7a7f74c47fa4d
- Size of remote file:
- 5.3 kB
- SHA256:
- 1b1d6a02b52716ce63c47616084b5270831d9c903ed961ad545bb6aa9c3f3bff
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