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
Update config.json
Browse files- config.json +3 -1
config.json
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{
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"_name_or_path": "dicta-il/dictabert",
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"architectures": [
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"BertForJointParsing"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"do_lex": true,
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{
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"architectures": [
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"BertForJointParsing"
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],
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"auto_map": {
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"AutoModel": "BertForJointParsing.BertForJointParsing"
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},
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"do_lex": true,
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