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SindBERT/
SindBERT_large

Name: SindBERT_large
Rating: 2.5 (19 reviews)

masked-language-modeling

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SindBERT: Charting the Seas of Turkish NLP

SindBERT is a family of RoBERTa-based Turkish language models pre-trained from scratch on ~312 GB of Turkish text from mC4, OSCAR23, and Wikipedia. The models aim to provide strong downstream performance for Turkish NLP and an openly available large-scale encoder for the community.

We release two variants:

SindBERT-base: 126M parameters (fp32)
SindBERT-large: 357M parameters (fp32)

Model Details

Detail	SindBERT-base	SindBERT-large
Architecture	RoBERTa-base	RoBERTa-large
Parameters	~126M	~357M
Tokenizer	GPT-2 style byte-level BPE (52,009 vocab)	Same
Pretraining corpus	Turkish mC4, OSCAR23, Wikipedia (~312 GB)	Same
Objective	Masked Language Modeling	Same
Training time	~29.2 hours (TPUv4-128 pod)	~6.0 days (TPUv4-128 pod)
Precision	fp32	fp32
Framework	fairseq	fairseq

Downstream Evaluation

We evaluate SindBERT on four Turkish benchmarks:

PoS tagging (Turkish UD concat): micro-F1
NER (WikiANN TR): micro-F1
Offensive language detection (OffensEval-TR 2020): macro-F1
Linguistic acceptability (TurBLiMP): average accuracy (16 phenomena)

🧪 Evaluation Results

Legend: Bold = best, italic = second-best per model size.

Model	PoS	NER	OffensEval-TR 2020	AVG core	TurBLiMP AVG	AVG all
Large models
SindBERT_large	94.63	93.64	82.29	90.19	89.8	90.09
XLM-R_large	94.39	94.44	81.99	90.27	92.7	90.73
EuroBERT_610M	93.33	91.85	75.57	86.92	90.0	87.84
Base models
ELECTRA_small	94.28	91.92	78.17	88.12	80.6	86.24
DistilBERTurk	94.01	91.54	79.19	88.25	87.2	87.99
ConvBERTurk	94.41	94.03	81.99	90.14	60.8	82.81
ConvBERTurk_mC4	94.57	93.56	81.90	90.01	55.5	81.38
ELECTRA_base	94.29	93.49	81.54	89.77	89.9	89.81
ELECTRA_mC4	94.40	93.43	81.38	89.74	89.9	89.78
BERTurk_32k	93.16	94.38	81.03	89.52	93.8	90.59
RoBERTurk	87.99	81.09	70.01	79.70	-	-
SindBERT_base	94.47	93.19	81.14	89.60	90.3	89.78
mmBERT_small	93.75	92.51	77.28	87.85	85.1	87.16
BERTurk_128k	94.44	93.81	81.77	90.01	95.1	91.28
EuroBERT_210M	92.97	90.91	75.73	86.54	86.3	86.48
XLM-R_base	94.23	92.90	79.77	88.97	89.2	89.03
mmBERT_base	93.75	93.35	78.49	88.53	89.3	88.72

Fairseq Checkpoint

Get the fairseq checkpoint here.

Citations

If you use SindBERT in your research, please cite the following paper:

bibtex

@inproceedings{schmitt-schweter-2026-sindbert,
    title = "{S}ind{BERT}, the Sailor: Charting the Seas of {T}urkish {NLP}",
    author = "Schmitt, Raphael  and
      Schweter, Stefan",
    editor = {Oflazer, Kemal  and
      K{\"o}ksal, Abdullatif  and
      Varol, Onur},
    booktitle = "Proceedings of the Second Workshop Natural Language Processing for {T}urkic Languages ({SIGTURK} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.sigturk-1.1/",
    doi = "10.18653/v1/2026.sigturk-1.1",
    pages = "1--13",
    ISBN = "979-8-89176-370-8"
}

📜 License

MIT License

SindBERT/SindBERT_large