Instructions to use Muhammad2003/router-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Muhammad2003/router-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Muhammad2003/router-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Muhammad2003/router-classifier") model = AutoModelForSequenceClassification.from_pretrained("Muhammad2003/router-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4781a1f2f9bd8158818cedff1b476a2a0580941010db3c2be74853b73cc6e65
- Size of remote file:
- 4.14 GB
- SHA256:
- 1be3dc4fb9978bdb62234e6076e5625f1b5b00a304c0eff0a84b0cf2b978edd7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.