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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT") model = AutoModelForMaskedLM.from_pretrained("avichr/heBERT", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from avichr/heBERT: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/avichr/heBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://avichr/heBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/avichr/heBERT/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 9c9d3ae8fe6b84c43b035babdab580dfeafc1a4c2f9554603a7761913d26a564
- Size of remote file:
- 438 MB
- SHA256:
- b219b9d76997d1933f01c362ae4fdc838600a5dcc5323869af1466959b74e6ed
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