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 lemmatization task.
For the bert-base models for other tasks, see here.
General guidelines for how the lemmatizer works:
Given an input text in Hebrew, it attempts to match up each word with the correct lexeme from within the BERT vocabulary.
If the word is split up into multiple wordpieces it doesn't cause a problem, we still predict the lexeme with a high accuracy.
If the lexeme of a given token doesn't appear in the vocabulary, the model will attempt to predict a special token
[BLANK]. In that case, the word is usually a name of a person or a city, and the lexeme is probably the word after removing prefixes which can be done with the dictabert-seg tool.For verbs the lexeme is the 3rd person past singular form.
This method is purely neural-based, so in rare instances the predicted lexeme may not be lexically related to the input, but rather a synonym selected from the same semantic space. To handle those edge cases one can implement a filter on top of the prediction to look at the top K matches and choose using a specific set of measures, such as edit distance, to choose the prediction that can more reasonably form a lexeme for the input word.
Sample usage:
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('dicta-il/dictabert-lex')
model = AutoModel.from_pretrained('dicta-il/dictabert-lex', 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:
[
[
[
"讘砖谞转",
"砖谞讛"
],
[
"1948",
"1948"
],
[
"讛砖诇讬诐",
"讛砖诇讬诐"
],
[
"讗驻专讬诐",
"讗驻专讬诐"
],
[
"拽讬砖讜谉",
"拽讬砖讜谉"
],
[
"讗转",
"讗转"
],
[
"诇讬诪讜讚讬讜",
"诇讬诪讜讚"
],
[
"讘驻讬住讜诇",
"驻讬住讜诇"
],
[
"诪转讻转",
"诪转讻转"
],
[
"讜讘转讜诇讚讜转",
"转讜诇讚讛"
],
[
"讛讗诪谞讜转",
"讗讜诪谞讜转"
],
[
"讜讛讞诇",
"讛讞诇"
],
[
"诇驻专住诐",
"驻专住诐"
],
[
"诪讗诪专讬诐",
"诪讗诪专"
],
[
"讛讜诪讜专讬住讟讬讬诐",
"讛讜诪讜专讬住讟讬"
]
]
]
Citation
If you use DictaBERT-lex in your research, please cite MRL Parsing without Tears: The Case of Hebrew
BibTeX:
@misc{shmidman2024mrl,
title={MRL Parsing Without Tears: The Case of Hebrew},
author={Shaltiel Shmidman and Avi Shmidman and Moshe Koppel and Reut Tsarfaty},
year={2024},
eprint={2403.06970},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
License
Shield: 
This work is licensed under a Creative Commons Attribution 4.0 International License.
