modernbert-3pair-adv-3label-clean
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: 0.3086
- Accuracy: 0.9337
- F1: 0.9336
- Precision: 0.9336
- Recall: 0.9337
- F1 Class 0: 0.9317
- Precision Class 0: 0.9316
- Recall Class 0: 0.9319
- F1 Class 1: 0.9555
- Precision Class 1: 0.9463
- Recall Class 1: 0.9649
- F1 Class 2: 0.9135
- Precision Class 2: 0.9230
- Recall Class 2: 0.9042
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: 8e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 6
- total_train_batch_size: 192
- total_eval_batch_size: 192
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 1
- label_smoothing_factor: 0.05
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | F1 Class 0 | Precision Class 0 | Recall Class 0 | F1 Class 1 | Precision Class 1 | Recall Class 1 | F1 Class 2 | Precision Class 2 | Recall Class 2 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1.8369 | 1.0 | 9697 | 0.3086 | 0.9337 | 0.9336 | 0.9336 | 0.9337 | 0.9317 | 0.9316 | 0.9319 | 0.9555 | 0.9463 | 0.9649 | 0.9135 | 0.9230 | 0.9042 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
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Model tree for upvantage/modernbert-3pair-adv-3label-clean
Base model
answerdotai/ModernBERT-large