Instructions to use netmatze/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use netmatze/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="netmatze/test_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("netmatze/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("netmatze/test_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ffef21249754a0312fbbfb80cee3969342b0281e3a2b27992245405a7240207
- Size of remote file:
- 4.86 kB
- SHA256:
- d9cff9c9c9ab2d576a9c8e2ab2717490a3ff92327fc835871b1cb63d6315e5ce
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.