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:
- 92eebc9cb68be5a8f243de05c6220d37715e416f3e37d9bb678a24a075356072
- Size of remote file:
- 433 MB
- SHA256:
- 11d3185830517df6f32f6770af1c9a95537bceef42d82fe902bc08a4b84439c4
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