File size: 2,609 Bytes
202b668
 
d4a3ab4
 
202b668
d4a3ab4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1d09167
d4a3ab4
 
 
 
 
 
 
 
1d09167
 
 
 
d4a3ab4
 
1d09167
 
 
 
 
d4a3ab4
 
1d09167
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d4a3ab4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
---
license: cc-by-4.0
language:
- he
---
# DictaBERT-Large: A State-of-the-Art BERT-Large Suite for Modern Hebrew

State-of-the-art language model for Hebrew, released [here](https://arxiv.org/abs/2308.16687).

This is the fine-tuned BERT-large model for the named-entity-recognition task.

For the bert-base models for other tasks, see [here](https://huggingface.co/collections/dicta-il/dictabert-6588e7cc08f83845fc42a18b).

For the bert-large models for other tasks, see [to-be-added].


Sample usage:

```python
from transformers import pipeline

oracle = pipeline('ner', model='dicta-il/dictabert-large-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 = '''讚讜讚 讘谉-讙讜专讬讜谉 (16 讘讗讜拽讟讜讘专 1886 - 讜' 讘讻住诇讜 转砖诇"讚) 讛讬讛 诪讚讬谞讗讬 讬砖专讗诇讬 讜专讗砖 讛诪诪砖诇讛 讛专讗砖讜谉 砖诇 诪讚讬谞转 讬砖专讗诇.'''
oracle(sentence)
```

Output:
```json
[
  {
    "entity_group": "PER",
    "score": 0.9998988,
    "word": "讚讜讚 讘谉 - 讙讜专讬讜谉",
    "start": 0,
    "end": 13
  },
  {
    "entity_group": "TIMEX",
    "score": 0.99989706,
    "word": "16 讘讗讜拽讟讜讘专 1886",
    "start": 15,
    "end": 31
  },
  {
    "entity_group": "TIMEX",
    "score": 0.99991614,
    "word": "讜' 讘讻住诇讜 转砖诇\"讚",
    "start": 34,
    "end": 48
  },
  {
    "entity_group": "TTL",
    "score": 0.9931756,
    "word": "讜专讗砖 讛诪诪砖诇讛",
    "start": 68,
    "end": 79
  },
  {
    "entity_group": "GPE",
    "score": 0.9995702,
    "word": "讬砖专讗诇",
    "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:**

```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: [![CC BY 4.0][cc-by-shield]][cc-by]

This work is licensed under a
[Creative Commons Attribution 4.0 International License][cc-by].

[![CC BY 4.0][cc-by-image]][cc-by]

[cc-by]: http://creativecommons.org/licenses/by/4.0/
[cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png
[cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg