dictabert-ner

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)

Output:

[
  {
    "entity_group": "PER",
    "score": 0.9999443,
    "word": "דוד בן - גוריון",
    "start": 0,
    "end": 13
  },
  {
    "entity_group": "TIMEX",
    "score": 0.99987966,
    "word": "16 באוקטובר 1886",
    "start": 15,
    "end": 31
  },
  {
    "entity_group": "TIMEX",
    "score": 0.9998579,
    "word": "ו' בכסלו תשל\"ד",
    "start": 34,
    "end": 48
  },
  {
    "entity_group": "TTL",
    "score": 0.99963045,
    "word": "וראש הממשלה",
    "start": 68,
    "end": 79
  },
  {
    "entity_group": "GPE",
    "score": 0.9997943,
    "word": "ישראל",
    "start": 96,
    "end": 101
  }
]

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.