ModernBERT-base-reference_AllLang2-Cefr2

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ModernBERT-base-reference_AllLang2-Cefr2

This model is a fine-tuned version of answerdotai/ModernBERT-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.1614
• F1: 0.7991

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3.6e-05
• train_batch_size: 3
• eval_batch_size: 3
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 48
• optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Training results

Framework versions

• Transformers 4.53.1
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.2

Citation

@inproceedings{alva-manchego-etal-2025-findings,
    title = "Findings of the {TSAR} 2025 Shared Task on Readability-Controlled Text Simplification",
    author = "Alva-Manchego, Fernando  and Stodden, Regina  and Imperial, Joseph Marvin  and Barayan, Abdullah  and North, Kai  and Tayyar Madabushi, Harish",
    editor = "Shardlow, Matthew  and Alva-Manchego, Fernando  and North, Kai  and Stodden, Regina  and Saggion, Horacio  and Khallaf, Nouran  and Hayakawa, Akio",
    booktitle = "Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.tsar-1.8/",
    doi = "10.18653/v1/2025.tsar-1.8",
    pages = "116--130",
    ISBN = "979-8-89176-176-6"
}