Instructions to use SuperSecureHuman/trainer_test_checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SuperSecureHuman/trainer_test_checkpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SuperSecureHuman/trainer_test_checkpoint")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SuperSecureHuman/trainer_test_checkpoint") model = AutoModelForSequenceClassification.from_pretrained("SuperSecureHuman/trainer_test_checkpoint") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3cfa627c15ad480cde206abe26a9ce1ce7c3c1a17f866fe2257f411e8ee52f79
- Size of remote file:
- 867 MB
- SHA256:
- 5048ccd942c1fa8f84859abf13b3def087a58f67d403eb31ee5407850c2b73f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.