Feature Extraction
Transformers
PyTorch
Safetensors
Hebrew
bert
custom_code
text-embeddings-inference
Instructions to use dicta-il/dictabert-seg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dicta-il/dictabert-seg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dicta-il/dictabert-seg", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dicta-il/dictabert-seg", trust_remote_code=True) model = AutoModel.from_pretrained("dicta-il/dictabert-seg", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f9943e7aa7522ef1023d0a0a5b47c9a18b9214fa4a0d92592d331334c6f750a
- Size of remote file:
- 740 MB
- SHA256:
- 7002724f1540e2f0e2caa5dbbcf1c8d77f4610d75b73474469a2d77f5cb2e9c2
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