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:
- 31477030076e643cbb265f7a67ac56ced9e20724eec143d4fb23f9fab5115a4f
- Size of remote file:
- 433 MB
- SHA256:
- 18d2b55007f104d6e60131af75395983b76aa1a6b95d2a0519cbaf88abdef22e
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