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)# 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
- Xet hash:
- fc2f7da0ce93ffa4852f08aecb6e8bc6d581f1cd201070cd1265f6eafeabd0be
- Size of remote file:
- 181 MB
- SHA256:
- 169154516731eb5a244137dfd2a5059875c9e2f74d6085237e96fc0436e7924d
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