modern-5-20-pairs-200k-labeled
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: 0.0631
- Accuracy: 0.9878
- F1: 0.9878
- Precision: 0.9878
- Recall: 0.9878
- F1 Class 0: 0.9879
- Precision Class 0: 0.9909
- Recall Class 0: 0.9849
- F1 Class 1: 0.9877
- Precision Class 1: 0.9846
- Recall Class 1: 0.9908
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: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 512
- total_eval_batch_size: 512
- 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
- label_smoothing_factor: 0.01
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.2445 | 1.0 | 588 | 0.0631 | 0.9878 | 0.9878 | 0.9878 | 0.9878 | 0.9879 | 0.9909 | 0.9849 | 0.9877 | 0.9846 | 0.9908 |
Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
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Base model
answerdotai/ModernBERT-large