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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbd3410d71da50fc4c3e934ed38dbb83828466cf27de9ce1c0012f0298e9f3a7
- Size of remote file:
- 1.78 kB
- SHA256:
- d028009bf2029df744e09caee6ef5c5fe830318d5e02b381011099c51f69c0d2
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