Instructions to use 9au5a/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 9au5a/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="9au5a/test_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("9au5a/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("9au5a/test_trainer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 887eca2313367f3914297c9d168b39c5398cdb4e521c2db0c7d5b87a5cfe5a3b
- Size of remote file:
- 4.03 kB
- SHA256:
- 2c7549c5ae8632554fb2ef6fd80057df4a062886e5ce42b3c0784ee8a986cf0e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.