Feature Extraction
Transformers
PyTorch
Safetensors
Hebrew
bert
custom_code
text-embeddings-inference
Instructions to use Hiveurban/dictabert-joint-handler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hiveurban/dictabert-joint-handler with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Hiveurban/dictabert-joint-handler", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Hiveurban/dictabert-joint-handler", trust_remote_code=True) model = AutoModel.from_pretrained("Hiveurban/dictabert-joint-handler", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0796543b7238caec5665bf4eabea5208fd16f7bd17cb3f69c6e6221915dd7acf
- Size of remote file:
- 1.41 kB
- SHA256:
- 3a43d803686eb8a83b54126497f66de6cc46cebe16ca004b68726bbb1b1aac36
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