Instructions to use jashdalvi/netnames-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jashdalvi/netnames-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jashdalvi/netnames-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jashdalvi/netnames-classifier") model = AutoModelForSequenceClassification.from_pretrained("jashdalvi/netnames-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f540bead931a2943a76ef1079a64e9235af8ac959360b01dac1706f9482e5d0b
- Size of remote file:
- 268 MB
- SHA256:
- 5371f2ff63dcd2542ff4d394d39a39f594b45fa769ab98d91f7f5c0cffc4228d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.