Instructions to use ania3000/ossbert-morph-5ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ania3000/ossbert-morph-5ep with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ania3000/ossbert-morph-5ep")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ania3000/ossbert-morph-5ep") model = AutoModelForTokenClassification.from_pretrained("ania3000/ossbert-morph-5ep") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 289781380f84e25ae5de4a7d4e3df62ab253719111798987b1a438328fe76bc7
- Size of remote file:
- 5.78 kB
- SHA256:
- ef5b0dcf726e1afcbb567aa2779017dd164d7beaaad847be7013720d51064ec8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.