metadata
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-Large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: modernBERT_clinc_oos
results: []
modernBERT_clinc_oos
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: 0.2510
- Accuracy: 0.9368
- F1 Macro: 0.9419
- Precision Macro: 0.9429
- Recall Macro: 0.9457
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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- 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: 0.05
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro |
|---|---|---|---|---|---|---|---|
| 16.366 | 1.0 | 625 | 0.3508 | 0.9155 | 0.9211 | 0.9240 | 0.9268 |
| 0.8392 | 2.0 | 1250 | 0.2510 | 0.9368 | 0.9419 | 0.9429 | 0.9457 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1