metadata
license: cc-by-4.0
language:
- he
inference: false
DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew
State-of-the-art language model for Hebrew, released here.
This is the fine-tuned model for the joint parsing of the following tasks:
- Prefix Segmentation
- Morphological Disabmgiuation
- Lexicographical Analysis (Lemmatization)
- Syntactical Parsing (Dependency-Tree)
- Named-Entity Recognition
For the bert-base models for other tasks, see here.
Sample usage:
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('dicta-il/dictabert-joint')
model = AutoModel.from_pretrained('dicta-il/dictabert-joint', trust_remote_code=True)
model.eval()
sentence = '讘砖谞转 1948 讛砖诇讬诐 讗驻专讬诐 拽讬砖讜谉 讗转 诇讬诪讜讚讬讜 讘驻讬住讜诇 诪转讻转 讜讘转讜诇讚讜转 讛讗诪谞讜转 讜讛讞诇 诇驻专住诐 诪讗诪专讬诐 讛讜诪讜专讬住讟讬讬诐'
print(model.predict([sentence], tokenizer))
Output:
TBD
Citation
If you use DictaBERT in your research, please cite DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew
BibTeX:
@misc{shmidman2023dictabert,
title={DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew},
author={Shaltiel Shmidman and Avi Shmidman and Moshe Koppel},
year={2023},
eprint={2308.16687},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
License
Shield: 
This work is licensed under a Creative Commons Attribution 4.0 International License.
