Instructions to use emanjavacas/MacBERTh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emanjavacas/MacBERTh with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("emanjavacas/MacBERTh", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6cb77212ddc40186019099dbeb020c552d4837b844172c59724e3da890380ab
- Size of remote file:
- 439 MB
- SHA256:
- 0dfba5777f622ab49fbef087eafe8618e280c1f5b303439509cf240f7b76ded8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.