metadata
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ModernBERT-large_v3
results: []
ModernBERT-large_v3
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: 0.8266
- Accuracy: 0.9109
- Precision Macro: 0.7681
- Recall Macro: 0.7438
- F1 Macro: 0.7542
- F1 Weighted: 0.9084
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted |
|---|---|---|---|---|---|---|---|---|
| 0.9719 | 1.0 | 179 | 0.4542 | 0.8484 | 0.7048 | 0.5982 | 0.5951 | 0.8301 |
| 0.6216 | 2.0 | 358 | 0.3472 | 0.8819 | 0.8897 | 0.6468 | 0.6648 | 0.8674 |
| 0.5377 | 3.0 | 537 | 0.3063 | 0.8926 | 0.7740 | 0.7326 | 0.7477 | 0.8893 |
| 0.4253 | 4.0 | 716 | 0.2703 | 0.9109 | 0.8330 | 0.7357 | 0.7651 | 0.9053 |
| 0.3699 | 5.0 | 895 | 0.2795 | 0.9090 | 0.7756 | 0.7968 | 0.7850 | 0.9107 |
| 0.2003 | 6.0 | 1074 | 0.3297 | 0.9128 | 0.8225 | 0.7620 | 0.7848 | 0.9094 |
| 0.1596 | 7.0 | 1253 | 0.3799 | 0.9097 | 0.7673 | 0.7805 | 0.7734 | 0.9109 |
| 0.0876 | 8.0 | 1432 | 0.5013 | 0.9236 | 0.8343 | 0.7899 | 0.8084 | 0.9214 |
| 0.0598 | 9.0 | 1611 | 0.5279 | 0.9185 | 0.8126 | 0.7621 | 0.7815 | 0.9152 |
| 0.054 | 10.0 | 1790 | 0.5909 | 0.9109 | 0.7998 | 0.7728 | 0.7847 | 0.9092 |
| 0.0419 | 11.0 | 1969 | 0.7661 | 0.9141 | 0.7877 | 0.7427 | 0.7594 | 0.9102 |
| 0.0108 | 12.0 | 2148 | 0.9184 | 0.9185 | 0.8260 | 0.7337 | 0.7601 | 0.9122 |
| 0.0177 | 13.0 | 2327 | 0.8254 | 0.9128 | 0.7820 | 0.7494 | 0.7628 | 0.9099 |
| 0.0013 | 14.0 | 2506 | 0.8059 | 0.9103 | 0.7741 | 0.7391 | 0.7531 | 0.9069 |
| 0.0019 | 15.0 | 2685 | 0.8174 | 0.9078 | 0.7620 | 0.7502 | 0.7556 | 0.9065 |
| 0.0028 | 16.0 | 2864 | 0.8202 | 0.9109 | 0.7704 | 0.7438 | 0.7550 | 0.9082 |
| 0.0 | 17.0 | 3043 | 0.8126 | 0.9103 | 0.7678 | 0.7433 | 0.7537 | 0.9078 |
| 0.0008 | 18.0 | 3222 | 0.8319 | 0.9109 | 0.7734 | 0.7482 | 0.7589 | 0.9085 |
| 0.0 | 19.0 | 3401 | 0.8245 | 0.9116 | 0.7686 | 0.7443 | 0.7546 | 0.9090 |
| 0.0001 | 20.0 | 3580 | 0.8266 | 0.9109 | 0.7681 | 0.7438 | 0.7542 | 0.9084 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.7.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4