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 flax_model.msgpack from avichr/heBERT: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/avichr/heBERT/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://avichr/heBERT/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/avichr/heBERT/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 28110f013457ddf4be326e47339ab92147538b149fd23df6db5df278dc60553b
- Size of remote file:
- 438 MB
- SHA256:
- 54148a17ee2cc8f73c175d6d9c0bef0c05d5f66524a9947dda093c327acd2ca8
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