metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: severity_model_checkpoints
results: []
severity_model_checkpoints
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2429
- Accuracy: 0.9151
- F1 Macro: 0.9154
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- 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: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| 1.0321 | 1.0 | 95 | 0.6955 | 0.8117 | 0.8053 |
| 0.2911 | 2.0 | 190 | 0.2814 | 0.9072 | 0.907 |
| 0.1628 | 3.0 | 285 | 0.2736 | 0.8939 | 0.8928 |
| 0.0522 | 4.0 | 380 | 0.2305 | 0.9151 | 0.916 |
| 0.0623 | 5.0 | 475 | 0.2429 | 0.9151 | 0.9154 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2