Model reference · open weights
dictabert-morph is an open-weight embedding model from dicta-il. dictabert-morph (FP32) weighs 369 MB; the smallest configuration that runs it is RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the dicta-il/dictabert-morph model card, 2026-10-01
What it is
| Released by | dicta-il |
|---|---|
| Released | 2023-08-29 |
| Parameters | 184M |
| VRAM | 369 MB for the weights |
What it runs on
| Card | Runs | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
State-of-the-art language model for Hebrew, released here.
This is the fine-tuned model for the morphological tagging task.
For the bert-base models for other tasks, see here.
Sample usage:
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('dicta-il/dictabert-morph')
model = AutoModel.from_pretrained('dicta-il/dictabert-morph', trust_remote_code=True)
model.eval()
sentence = 'בשנת 1948 השלים אפרים קישון את לימודיו בפיסול מתכת ובתולדות האמנות והחל לפרסם מאמרים הומוריסטיים'
print(model.predict([sentence], tokenizer))
Output:
[{
"text": "בשנת 1948 השלים אפרים קישון את לימודיו בפיסול מתכת ובתולדות האמנות והחל לפרסם מאמרים הומוריסטיים",
"tokens": [{
"token": "בשנת",
"pos": "NOUN",
"feats": {
"Gender": "Fem",
"Number": "Sing"
},
"prefixes": ["ADP"],
"suffix": false
}, {
"token": "1948",
"pos": "NUM",
"feats": {},
"prefixes": [],
"suffix": false
}, {
"token": "השלים",
"pos": "VERB",
"feats": {
"Gender": "Masc",
"Number": "Sing",
"Person": "3",
"Tense": "Past"
},
"prefixes": [],
"suffix": false
}, {
"token": "אפרים",
"pos": "PROPN",
"feats": {},
"prefixes": [],
"suffix": false
}, {
"token": "קישון",
"pos": "PROPN",
"feats": {},
"prefixes": [],
"suffix": false
}, {
"token": "את",
"pos": "ADP",
"feats": {},
"prefixes": [],
"suffix": false
}, {
"token": "לימודיו",
"pos": "NOUN",
"feats": {
"Gender": "Masc",
"Number": "Plur"
},
"prefixes": [],
"suffix": "PRON",
"suffix_feats": {
"Gender": "Masc",
"Number": "Sing",
"Person": "3"
}
}, {
"token": "בפיסול",
"pos": "NOUN",
"feats": {
"Gender": "Masc",
"Number": "Sing"
},
"prefixes": ["ADP"],
"suffix": false
}, {
"token": "מתכת",
"pos": "NOUN",
"feats": {
"Gender": "Fem",
"Number": "Sing"
},
"prefixes": [],
"suffix": false
}, {
"token": "ובתולדות",
"pos": "NOUN",
"feats": {
"Gender": "Fem",
"Number": "Plur"
},
"prefixes": ["CCONJ", "ADP"],
"suffix": false
}, {
"token": "האמנות",
"pos": "NOUN",
"feats": {
"Gender": "Fem",
"Number": "Sing"
},
"prefixes": ["DET"],
"suffix": false
}, {
"token": "והחל",
"pos": "VERB",
"feats": {
"Gender": "Masc",
"Number": "Sing",
"Person": "3",
"Tense": "Past"
},
"prefixes": ["CCONJ"],
"suffix": false
}, {
"token": "לפרסם",
"pos": "VERB",
"feats": {},
"prefixes": [],
"suffix": false
}, {
"token": "מאמרים",
"pos": "NOUN",
"feats": {
"Gender": "Masc",
"Number": "Plur"
},
"prefixes": [],
"suffix": false
}, {
"token": "הומוריסטיים",
"pos": "ADJ",
"feats": {
"Gender": "Masc",
"Number": "Plur"
},
"prefixes": [],
"suffix": false
}]
}]
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}
}
This work is licensed under a Creative Commons Attribution 4.0 International License.
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.