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Model card
From the publisher at commit 4f0aabf. Shown as plain text with links; images open at the publisher.
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 BERT-base model for the named-entity-recognition task.
For the bert-base models for other tasks, see here.
Sample usage:
from transformers import pipeline
oracle = pipeline('ner', model='dicta-il/dictabert-ner', aggregation_strategy='simple')
# if we set aggregation_strategy to simple, we need to define a decoder for the tokenizer. Note that the last wordpiece of a group will still be emitted
from tokenizers.decoders import WordPiece
oracle.tokenizer.backend_tokenizer.decoder = WordPiece()
sentence = '''דוד בן-גוריון (16 באוקטובר 1886 - ו' בכסלו תשל"ד) היה מדינאי ישראלי וראש הממשלה הראשון של מדינת ישראל.'''
oracle(sentence)
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}
}
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