Instructions to use jndvs/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jndvs/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jndvs/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jndvs/results") model = AutoModelForSequenceClassification.from_pretrained("jndvs/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4cd7b4a172801d92c6ec932fa2b18c6337809c56852c9a6b59bca85beb55b532
- Size of remote file:
- 439 MB
- SHA256:
- d15f83002e1ea04ff4fa71a63d169106f2f69af080541265f3aeaf7f93c2b706
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