Feature Extraction
Transformers
PyTorch
Safetensors
Hebrew
bert
custom_code
text-embeddings-inference
Instructions to use dicta-il/dictabert-morph with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dicta-il/dictabert-morph with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dicta-il/dictabert-morph", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dicta-il/dictabert-morph", trust_remote_code=True) model = AutoModel.from_pretrained("dicta-il/dictabert-morph", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a2a16efd66113b6a42ff9a4ed0f16465c0faf9865ffe40dbcf74e971ae34055
- Size of remote file:
- 738 MB
- SHA256:
- a9516849580201b58a84fa1859624fa91fa425d9b0046e6c31cc436873c01dd5
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