Instructions to use ania3000/kuosbert-morph with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ania3000/kuosbert-morph with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ania3000/kuosbert-morph")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ania3000/kuosbert-morph") model = AutoModelForTokenClassification.from_pretrained("ania3000/kuosbert-morph", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50c7c1cc915b65906e826654f04252c11028cfb5de82ed59fac87c5b5237a634
- Size of remote file:
- 5.84 kB
- SHA256:
- 15fb197cfd53ca5faeb4052cdfebb34b52b68a6341ff2c495cbf7b89e85945a4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.