Feature Extraction
Transformers
PyTorch
Safetensors
Hebrew
bert
custom_code
text-embeddings-inference
Instructions to use dicta-il/dictabert-joint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dicta-il/dictabert-joint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dicta-il/dictabert-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-joint", trust_remote_code=True) model = AutoModel.from_pretrained("dicta-il/dictabert-joint", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dicta-il/dictabert-joint: direct link, hf CLI and curl.
- Browser
- Download file 744 MB
-
https://huggingface.co/dicta-il/dictabert-joint/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dicta-il/dictabert-joint/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dicta-il/dictabert-joint/resolve/main/pytorch_model.bin
744 MB
- Xet hash:
- 854f90976eafe591112ee0babcc27ef63764fd4b3229a99ceb5ad56f3ee1d0b7
- Size of remote file:
- 744 MB
- SHA256:
- 26ce128baa792901b22cecfdd6a7dc783307e60d4b766dcd3aa4d1eaeb3a36d2
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