Instructions to use ukk2397/temp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ukk2397/temp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ukk2397/temp")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ukk2397/temp") model = AutoModelForSequenceClassification.from_pretrained("ukk2397/temp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 257f2653865192208d61f17b511a6b4ae1e184b4d2822f13b7f86a4a712bfd48
- Size of remote file:
- 4.86 kB
- SHA256:
- cf7a11ec6f4c2156a76eeca0c5712933ca859309cef5d7765a0d6953782b03e0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.