Feature Extraction
Transformers
PyTorch
Safetensors
Hebrew
bert
custom_code
text-embeddings-inference
Instructions to use dicta-il/dictabert-tiny-joint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dicta-il/dictabert-tiny-joint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dicta-il/dictabert-tiny-joint", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dicta-il/dictabert-tiny-joint", trust_remote_code=True) model = AutoModel.from_pretrained("dicta-il/dictabert-tiny-joint", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from dicta-il/dictabert-tiny-joint: direct link, hf CLI and curl.
- Browser
- Download file 181 MB
-
https://huggingface.co/dicta-il/dictabert-tiny-joint/resolve/main/model.safetensors
- Command line
-
hf download hf://dicta-il/dictabert-tiny-joint/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/dicta-il/dictabert-tiny-joint/resolve/main/model.safetensors
181 MB
- Xet hash:
- 1c31138f7734b0bd0bce4bafc5b2de6a1f6530c11daab1a9cf1ef85585e1cda8
- Size of remote file:
- 181 MB
- SHA256:
- 577997e9eded405f391d77db60072c0d1bcf37354865aacfdb514297288c6f90
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