Instructions to use Stepa/my_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Stepa/my_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Stepa/my_model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Stepa/my_model") model = AutoModelForTokenClassification.from_pretrained("Stepa/my_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d76c0757aaa5a7e690097ad42bcd3e0c1d42da6622c3b59d5387bf37ec4c1d5e
- Size of remote file:
- 3.52 kB
- SHA256:
- 7202535d2320b94bc371f9ec2022476c1179ef775d41ebe4dedfb4fcf13a848b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.