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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SuperSecureHuman/trainer_test_checkpoint") model = AutoModelForSequenceClassification.from_pretrained("SuperSecureHuman/trainer_test_checkpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-55/training_args.bin from SuperSecureHuman/trainer_test_checkpoint: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/SuperSecureHuman/trainer_test_checkpoint/resolve/main/checkpoint-55/training_args.bin
- Command line
-
hf download hf://SuperSecureHuman/trainer_test_checkpoint/checkpoint-55/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SuperSecureHuman/trainer_test_checkpoint/resolve/main/checkpoint-55/training_args.bin
3.96 kB
- Xet hash:
- b043172c565b3d9ab4d8445499463b92c596630db299cfdf2148037bd9c37498
- Size of remote file:
- 3.96 kB
- SHA256:
- f994e774f37ded33f3ebe64d3be2318670c8aaf6b944d3b012a125b991f2869c
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