model-combined-512-fortraining-1.6m
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: 0.0362
- Accuracy: 0.9906
- F1: 0.9906
- Precision: 0.9907
- Recall: 0.9906
- F1 Class 0: 0.9907
- Precision Class 0: 0.9852
- Recall Class 0: 0.9962
- F1 Class 1: 0.9906
- Precision Class 1: 0.9962
- Recall Class 1: 0.9850
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: 1e-05
- train_batch_size: 200
- eval_batch_size: 200
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 1600
- total_eval_batch_size: 1600
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.3015 | 1.0 | 1106 | 0.0362 | 0.9906 | 0.9906 | 0.9907 | 0.9906 | 0.9907 | 0.9852 | 0.9962 | 0.9906 | 0.9962 | 0.9850 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
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Base model
answerdotai/ModernBERT-large