Instructions to use Nav772/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nav772/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nav772/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Nav772/results") model = AutoModelForSequenceClassification.from_pretrained("Nav772/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 572acf56077bfa7b5de0395ecd91cdd7467283792d4dec91ef31067d6f0a083c
- Size of remote file:
- 5.84 kB
- SHA256:
- dffd664bfd1c5be62d4dcc460a9937aa98d5162cb63773cb846b2ab8ca2a6670
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.