CoolFace
Modelpublic

OPI-PIB/pl-ModernBERT-large

sourceHugging Faceapache-2.0updated 10d agoView on Hugging Face
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Model Card

<h1 align="center">Polish ModernBERT Large</h1>

Polish ModernBERT is a family of monolingual ModernBERT-based encoders pretrained for Polish. The family covers two model scales (Base and Large) and two context lengths (512 and 8,192 tokens). The models are general-purpose pretrained encoders intended to serve as strong foundations for a broad range of Polish NLP applications. They can be fine-tuned or adapted for downstream tasks including, but not limited to, text and document classification, sequence labeling, regression, semantic similarity, retrieval, reranking, and representation learning.

📄 Paper: Polish ModernBERT: The Long and Short of Polish Language Understanding 🤗 Model collection: Polish ModernBERT 📚 LongContext benchmark: Dataset

Model family

ModelParametersContextVocabularyTokenizer
`pl-ModernBERT-512-base`149M51250,008SentencePiece Unigram
`pl-ModernBERT-512-large`475M512128,256SentencePiece Unigram + byte fallback
`pl-ModernBERT-base`149M8,19250,008SentencePiece Unigram
`pl-ModernBERT-large`475M8,192128,256SentencePiece Unigram + byte fallback

All variants use the core ModernBERT architecture with RoPE, GeGLU feed-forward layers, pre-normalization, and alternating global/local attention. The Base models use 22 layers with hidden size 768, while the Large models use 28 layers with hidden size 1,024.

Training

The models were pretrained on approximately 44.5B Polish tokens (197 GB after preprocessing) from a curated Polish corpus, Common Crawl (CC-MAIN-2019-43), and the Polish pol_Latn subset of FineTranslations.

Pretraining follows a staged recipe selected through downstream validation: four 512-token stages progressively transition from token-level MLM to whole-word masking, increased emphasis on curated data, and final annealing, followed by long-context continuation to 8,192 tokens. During context extension, the global RoPE theta is increased from 10,000 to 160,000.

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'/> <em>Pretraining schedule used for Polish ModernBERT. The final 512-token checkpoints initialize the corresponding 8K variants.</em> </center>

Training was performed with Composer in BF16 using StableAdamW and distributed data parallelism across 8 NVIDIA GH200 GPUs.

Evaluation

The models were evaluated on 30 Polish NLU tasks spanning KLEJ, FinBench, the five-task LongContext benchmark, and a range of additional Polish NLP tasks. Each model-task configuration was fine-tuned using five random seeds; scores below are means on a 0–100 scale.

Evaluation Results

The tables below summarize average performance across the main evaluation groups. Scores are reported on a 0–100 scale. Overall Avg. is the macro-average across all 30 individual tasks rather than the average of the four group-level scores.

Base models
ModelContextKLEJ Avg.FinBench Avg.Other Tasks Avg.LongContext Avg.Overall Avg.
XLM-R Base51284.4683.1377.1050.7576.12
HerBERT Base51285.8383.6579.2861.9379.23
Polish RoBERTa-v2 Base51286.7585.1480.2657.6679.42
Polish ModernBERT 512 Base51286.8486.9582.0771.5982.73
EuroBERT-210M8K77.1682.3873.7172.3076.24
mmBERT Small8K80.1183.0778.0169.0278.15
Polish RoBERTa-8K Base8K86.8685.9882.3667.4781.95
Polish ModernBERT Base8K87.0086.6983.0877.1583.99
Large models
ModelContextKLEJ Avg.FinBench Avg.Other Tasks Avg.LongContext Avg.Overall Avg.
XLM-R Large51287.2985.4482.6467.8782.13
HerBERT Large51287.8387.3383.4270.2683.33
Polish RoBERTa-v2 Large51288.6987.9083.5870.4983.80
Polish ModernBERT 512 Large51288.4888.1883.6073.5884.31
EuroBERT-610M8K80.1086.1178.9577.3680.46
mmBERT Base8K83.1785.6980.8073.4481.27
Polish RoBERTa-8K Large8K88.5287.9884.2775.8884.89
Polish ModernBERT Large8K88.3187.8383.9078.4985.11

KLEJ

Polish ModernBERT is competitive with the strongest Polish BERT/RoBERTa encoders on KLEJ. The Base variants obtain the highest KLEJ average in both context settings, while the Large variants remain within 0.21 points of the corresponding Polish RoBERTa models.

<details> <summary><b>Detailed KLEJ results - Base models</b></summary>

Taskpl-RoBERTa-v2-basepl-ModernBERT-512-basepl-RoBERTa-8K-basepl-ModernBERT-base
NKJP-NER94.3294.3894.1694.49
CDSC-E94.0594.6694.5494.46
CDSC-R94.6494.1194.9094.06
CBD70.5768.5669.3571.40
POLEMO-IN90.9792.8891.2792.14
POLEMO-OUT79.1183.7781.2683.04
DYK70.3866.9069.3567.28
PSC98.8897.7998.9097.68
AR87.8388.5388.0588.46
Average86.7586.8486.8687.00

</details>

<details> <summary><b>Detailed KLEJ results - Large models</b></summary>

Taskpl-RoBERTa-v2-largepl-ModernBERT-512-largepl-RoBERTa-8K-largepl-ModernBERT-large
NKJP-NER95.7595.0595.6494.38
CDSC-E94.1694.6094.2894.68
CDSC-R95.2595.1495.3394.47
CBD73.1072.6073.2371.47
POLEMO-IN93.5593.3893.0593.05
POLEMO-OUT83.8184.4183.6484.78
DYK74.8773.2874.0574.63
PSC98.3798.8198.5698.47
AR89.3689.0788.9188.88
Average88.6988.4888.5288.31

</details>

FinBench

Polish ModernBERT shows particularly strong performance on FinBench. The Base variants achieve the highest FinBench average in both context settings. At Large scale, the 512-token Polish ModernBERT obtains the highest average, while the 8K variant remains close to the corresponding Polish RoBERTa model.

<details> <summary><b>Detailed FinBench results - Base models</b></summary>

Taskpl-RoBERTa-v2-basepl-ModernBERT-512-basepl-RoBERTa-8K-basepl-ModernBERT-base
Banking-Short78.7580.4179.7980.08
Banking-Long85.0387.2986.9987.16
Banking7788.2691.8589.2791.66
FPB83.5583.2083.6383.40
GCN95.0294.8794.8794.83
Stooq80.2584.0881.3283.03
Average85.1486.9585.9886.69

</details>

<details> <summary><b>Detailed FinBench results - Large models</b></summary>

Taskpl-RoBERTa-v2-largepl-ModernBERT-512-largepl-RoBERTa-8K-largepl-ModernBERT-large
Banking-Short81.6982.0781.9981.94
Banking-Long87.8988.4088.3588.89
Banking7792.4592.9692.7492.62
FPB85.2684.8085.4284.60
GCN95.0495.0894.9794.88
Stooq85.0785.7784.4184.02
Average87.9088.1887.9887.83

</details>

Other Tasks

Across the additional Polish NLP tasks, the Base variants of Polish ModernBERT obtain the highest average in both context settings. At Large scale, the 512-token model achieves the highest average, while the 8K variant remains competitive with the corresponding Polish RoBERTa model.

<details> <summary><b>Detailed Other Tasks results - Base models</b></summary>

Taskpl-RoBERTa-v2-basepl-ModernBERT-512-basepl-RoBERTa-8K-basepl-ModernBERT-base
8TAGS78.0380.6979.2180.86
BAN-PL92.1993.1092.6293.08
MIPD58.5867.1164.3968.03
PPC87.0584.4086.0285.70
SICK-E86.7186.3186.3186.61
SICK-R82.5883.1683.1683.77
TwitterEMO66.4669.0268.7569.52
IMDB91.0692.0595.0294.40
EURLEX74.5179.3179.1279.61
NKJP-NER*85.4185.5488.9789.21
Average80.2682.0782.3683.08

</details>

<details> <summary><b>Detailed Other Tasks results - Large models</b></summary>

Taskpl-RoBERTa-v2-largepl-ModernBERT-512-largepl-RoBERTa-8K-largepl-ModernBERT-large
8TAGS81.6482.5081.4482.24
BAN-PL93.8094.0093.9993.51
MIPD67.2768.2868.5068.99
PPC89.9688.0489.4887.20
SICK-E88.3387.8888.9687.47
SICK-R85.9384.6986.5484.91
TwitterEMO70.7070.2070.6070.35
IMDB94.3693.7796.0395.93
EURLEX79.1979.8479.7779.76
NKJP-NER*84.6286.8487.3688.66
Average83.5883.6084.2783.90

</details>

Long-context performance

Polish ModernBERT shows its largest gains on the LongContext benchmark, which consists of five tasks designed specifically to evaluate long-document understanding. The 8K variants achieve the highest LongContext average at both model scales.

At Base scale, pl-ModernBERT-base improves over pl-RoBERTa-8K-base by 9.68 points (77.15 vs. 67.47) while using 22% fewer parameters (149M vs. 190M). At Large scale, pl-ModernBERT-large improves over the corresponding Polish RoBERTa-8K baseline by 2.61 points (78.49 vs. 75.88).

<details> <summary><b>Detailed LongContext results - Base models</b></summary>

Taskpl-RoBERTa-v2-basepl-ModernBERT-512-basepl-RoBERTa-8K-basepl-ModernBERT-base
SCOTUS-Dom79.1282.8179.2684.48
SCOTUS-Dec69.8670.7463.2077.79
BookSummary85.0283.7188.9690.22
ECtHR-PL-AVA33.6561.4264.6668.01
ECtHR-PL-VA20.6559.2941.2865.27
Average57.6671.5967.4777.15

</details>

<details> <summary><b>Detailed LongContext results - Large models</b></summary>

Taskpl-RoBERTa-v2-largepl-ModernBERT-512-largepl-RoBERTa-8K-largepl-ModernBERT-large
SCOTUS-Dom83.0183.8383.9985.78
SCOTUS-Dec72.4671.3968.7378.21
BookSummary87.4786.5493.1191.74
ECtHR-PL-AVA58.3364.6268.2969.48
ECtHR-PL-VA51.1761.5065.2767.24
Average70.4973.5875.8878.49

</details>

The 512-token variants are evaluated on LongContext with truncation and should be treated as practical short-context baselines rather than controlled context-length ablations.

Inference efficiency

In a common BF16 inference setup on a single NVIDIA H100, Polish ModernBERT provides favorable quality–efficiency trade-offs relative to the corresponding Polish RoBERTa baselines.

For 512-token inference, the Base and Large models reduce latency by approximately 26% and 47%, respectively, while reducing peak GPU memory usage by 54% and 18%. In the 8K setting, the corresponding latency reductions are 6% and 25%, with peak memory reductions of 24% and 21%.

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"/> <em>Quality–efficiency trade-offs for Polish ModernBERT and the evaluated encoder baselines. The x-axis shows inference latency per sample, the y-axis shows downstream performance, and marker size represents peak GPU memory usage. Measurements were performed in BF16 on a single NVIDIA H100 GPU.</em> </center>

Usage

python
from transformers import AutoTokenizer, AutoModel

model_id = "OPI-PIB/pl-ModernBERT-large"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id)

text = "W białodrzewiu jaśnie dźni słoneczno, miodzie złoci białopałem żyśnie, drzewia pełni pszczelą i pasieczną, a przez liście kraśnie pęk słowiśnie."

inputs = tokenizer(
    text,
    return_tensors="pt",
    truncation=True,
    max_length=8192,
)

outputs = model(**inputs)
token_embeddings = outputs.last_hidden_state

For downstream tasks such as classification, regression, sequence labeling, retrieval, or reranking, the pretrained encoder can be adapted to the target task.

The raw checkpoints are not retrieval-specific sentence-embedding models. PIRB retrieval results reported in the paper were obtained after contrastive fine-tuning.

For longer inputs, use one of the corresponding 8K checkpoints from the Polish ModernBERT family.

Flash Attention 2

🏎️ For the highest training and inference efficiency, we recommend using Polish ModernBERT with Flash Attention 2 when supported by your GPU and environment.

bash
pip install flash-attn --no-build-isolation

The model can then be loaded with:

python
from transformers import AutoModel

model = AutoModel.from_pretrained(
    model_id,
    attn_implementation="flash_attention_2",
    torch_dtype="auto",
)

Authors

Michał Perełkiewicz, Sławomir Dadas, Rafał Poświata, Małgorzata Grębowiec

AI Lab, National Information Processing Institute (Ośrodek Przetwarzania Informacji – Państwowy Instytut Badawczy, OPI PIB) Warsaw, Poland

Corresponding author: mperelkiewicz@opi.org.pl

Acknowledgments

This work was supported by the Gaia AI Factory project, funded by the European Union under Grant Agreement No. 101314359 through the EuroHPC Joint Undertaking (EuroHPC JU).

We gratefully acknowledge the Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computational resources and support within computational grant PLG/2025/018315.

Citation

If you use Polish ModernBERT in your work, please cite:

bibtex
@misc{perełkiewicz2026polishmodernbertlongshort,
      title={Polish ModernBERT: The Long and Short of Polish Language Understanding}, 
      author={Michał Perełkiewicz and Sławomir Dadas and Rafał Poświata and Małgorzata Grębowiec},
      year={2026},
      eprint={2609.01379},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2609.01379}, 
}