Instructions to use avichr/heBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avichr/heBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="avichr/heBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT") model = AutoModelForMaskedLM.from_pretrained("avichr/heBERT") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28110f013457ddf4be326e47339ab92147538b149fd23df6db5df278dc60553b
- Size of remote file:
- 438 MB
- SHA256:
- 54148a17ee2cc8f73c175d6d9c0bef0c05d5f66524a9947dda093c327acd2ca8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.