Instructions to use RamuS/test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RamuS/test_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RamuS/test_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RamuS/test_model") model = AutoModelForSequenceClassification.from_pretrained("RamuS/test_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7267e71e71c4fa0ce6ba1fbf31697d6d04ac40f5f8516a82ffa6e096deb9ca9f
- Size of remote file:
- 1.34 GB
- SHA256:
- fdd727b3b4f0573e6f0c8eb6fbb914f52ecdd478badf793ea58076d3f71409e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.