Instructions to use lgessler/microbert-coptic-mx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lgessler/microbert-coptic-mx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lgessler/microbert-coptic-mx")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lgessler/microbert-coptic-mx") model = AutoModel.from_pretrained("lgessler/microbert-coptic-mx") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d10ff96216012c7c3c2f4a96c7b75eb1884062c548013d886305f6366ed20d8
- Size of remote file:
- 5.17 MB
- SHA256:
- 49f4d6e3959818b7187392e3e4f9bae87fe914a4c107e755a780a43e8df79913
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.