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modernBERT-finetuned
Anixyz/modernBERT-finetuned
modernBERT-finetuned is a text classification model from Anixyz. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- modernbert
- Access
- Public
- Created
- Oct 14, 2025
- Updated
- Oct 14, 2025
- SHA
- a9f9e02b
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
modernBERT-finetuned
This model is a fine-tuned version of answerdotai/ModernBERT-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4343
- Accuracy: 0.9149
- F1: 0.9122
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 2.2837 | 0.2096 | 100 | 0.9484 | 0.7671 | 0.7603 |
| 0.3814 | 0.4193 | 200 | 0.6216 | 0.8545 | 0.8498 |
| 0.2401 | 0.6289 | 300 | 0.6264 | 0.8569 | 0.8495 |
| 0.2168 | 0.8386 | 400 | 0.5532 | 0.872 | 0.8706 |
| 0.1421 | 1.0482 | 500 | 0.5101 | 0.8875 | 0.8844 |
| 0.0653 | 1.2579 | 600 | 0.5893 | 0.8824 | 0.8769 |
| 0.0567 | 1.4675 | 700 | 0.5224 | 0.8935 | 0.8914 |
| 0.0593 | 1.6771 | 800 | 0.5689 | 0.8849 | 0.8808 |
| 0.0598 | 1.8868 | 900 | 0.5895 | 0.886 | 0.8827 |
| 0.0518 | 2.0964 | 1000 | 0.6355 | 0.8767 | 0.8682 |
| 0.025 | 2.3061 | 1100 | 0.5616 | 0.8915 | 0.8861 |
| 0.0182 | 2.5157 | 1200 | 0.4563 | 0.9073 | 0.9040 |
| 0.0241 | 2.7254 | 1300 | 0.4912 | 0.9042 | 0.9011 |
| 0.0174 | 2.9350 | 1400 | 0.4381 | 0.9135 | 0.9112 |
| 0.0164 | 3.1447 | 1500 | 0.4792 | 0.9076 | 0.9042 |
| 0.0091 | 3.3543 | 1600 | 0.5133 | 0.9011 | 0.8969 |
| 0.0111 | 3.5639 | 1700 | 0.5006 | 0.9044 | 0.8997 |
| 0.0096 | 3.7736 | 1800 | 0.4089 | 0.9189 | 0.9170 |
| 0.004 | 3.9832 | 1900 | 0.4024 | 0.9195 | 0.9174 |
| 0.002 | 4.1929 | 2000 | 0.4174 | 0.9173 | 0.9150 |
| 0.0023 | 4.4025 | 2100 | 0.4248 | 0.9169 | 0.9145 |
| 0.0006 | 4.6122 | 2200 | 0.4360 | 0.9149 | 0.9122 |
| 0.0028 | 4.8218 | 2300 | 0.4343 | 0.9149 | 0.9122 |
Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- modernbert
- License
- apache-2.0
- Created
- Oct 14, 2025
- Updated
- Oct 14, 2025