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:
- b8ed7355bf2d97dfda150062490b3af65f5ea862a853f6cd2c597432958b21d4
- Size of remote file:
- 2.99 kB
- SHA256:
- 4bf54a572cdc3d330b4ae5549bc176845197f5320b2364b54572b4164b323c6a
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