Instructions to use lgessler/microbert-coptic-mxp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lgessler/microbert-coptic-mxp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lgessler/microbert-coptic-mxp")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lgessler/microbert-coptic-mxp") model = AutoModel.from_pretrained("lgessler/microbert-coptic-mxp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5987cebb1f8e04e463e0bfbd59f48f49484ffded245d2756ea69cbc4c00c4a17
- Size of remote file:
- 5.17 MB
- SHA256:
- 2523d62b4b89b3a8fade63ac1df7ab837eab2ac1f82a815f99ffe98724e7e2c3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.