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:
- 40f7c5f5b7443e16f55e1f887b9428e1bcf4f4857c9f2f0ad4468b0ff03629db
- Size of remote file:
- 867 MB
- SHA256:
- d86d374b5fd5d0a5a4a9f01466feaed7536d145d760372ad8bc5a8cad85cd4b8
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