Instructions to use avichr/Legal-heBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avichr/Legal-heBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="avichr/Legal-heBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("avichr/Legal-heBERT") model = AutoModelForMaskedLM.from_pretrained("avichr/Legal-heBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e37ed0f10e9e943a595c2b96d1cf665973fb45fa560c4f7cc6079c784d8b1d49
- Size of remote file:
- 498 MB
- SHA256:
- 9307be9036fe550fd76d83e60ba0f1233f96eb2c09648e6ceb8764e5f234fb5e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.