Feature Extraction
Transformers
PyTorch
Safetensors
Hebrew
bert
custom_code
text-embeddings-inference
Instructions to use dicta-il/dictabert-lex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dicta-il/dictabert-lex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dicta-il/dictabert-lex", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dicta-il/dictabert-lex", trust_remote_code=True) model = AutoModel.from_pretrained("dicta-il/dictabert-lex", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c1f60fb94c758b19a6f79a4b91cc55f3d5d66f250377cc7470e342ff052a8dc
- Size of remote file:
- 738 MB
- SHA256:
- d5c161077b87d8ee0bb8c9faba39721f6bfec76d60297a322f9532200b6e31a7
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