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
Commit History
-
Sync modeling code from FinetuningBert@ebbd87d466c6
Verified signature3c3c270
-
Sync from 0bb0e6d in FinetuningBert on GitHub (#7)
- Sync from 66b3fde in FinetuningBert on GitHub (026c61a877cdec918ece3e1411b8b785afb653b1) - Fix trust_remote_code loading + multi-label dependency head indexing (9d0eccc9a093e4984899a51245dcc1005c291b4f)c892da4
-
Update BertForSyntaxParsing.py
Verified signatured575ce5
-
Fixed bug with UNK tokens being discarded causing misalignment.
Verified signaturedeb5cae
-
Added major speedup
2565711
-
Upload 2 files
Verified signaturec397e3d
-
Upload 4 files
Verified signature2618264