Instructions to use superlazycoder/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use superlazycoder/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="superlazycoder/test_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("superlazycoder/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("superlazycoder/test_trainer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3519f81475fc0da146b14d929cdf0db93e4617acd1d3e49cc733dee903247b0d
- Size of remote file:
- 433 MB
- SHA256:
- 2e2a82f7155c93e3bb6e0db313e19946ea35f45874e0f017f2e53547a576f626
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.