DictaBERT-char: A Character-Level BERT-Base model for Hebrew.
DictaBERT-char is a BERT-style language model for Hebrew, based on the BERT-base architecture with a character level tokenizer. The model based on the BERT-Large architecture is available
here
.
This model is released to the public in this 2025 W-NUT paper: Avi Shmidman and Shaltiel Shmidman, "Restoring Missing Spaces in Scraped Hebrew Social Media", The 10th Workshop on Noisy and User-generated Text (W-NUT), 2025
This is the base model pretrained with the masked-language-modeling objective.
Sample usage:
from transformers import AutoModelForMaskedLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('dicta-il/dictabert-char')
model = AutoModelForMaskedLM.from_pretrained('dicta-il/dictabert-char')
model.eval()
sentence = 'בשנת 1948 השלים אפרים קישון את מחקרו בפיסול מתכת וב[MASK]ולדות האמנות והחל לפרסם מאמרים הומוריסטיים'
output = model(tokenizer.encode(sentence, return_tensors='pt'))
# the [MASK] is the 52nd token (including [CLS])import torch
top_arg = torch.argmax(output.logits[0, 52, :])
print(tokenizer.convert_ids_to_tokens([top_arg])) # should print ['ת']
Citation
If you use DictaBERT-char in your research, please cite
Restoring Missing Spaces in Scraped Hebrew Social Media
BibTeX:
@inproceedings{shmidman2025restoring,
author = {Avi Shmidman and Shaltiel Shmidman},
title = {Restoring Missing Spaces in Scraped Hebrew Social Media},
booktitle = {The 10th Workshop on Noisy and User-generated Text (W-NUT)},
year = {2025}
}
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