| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: jim-crow-test |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # jim-crow-test |
|
|
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1308 |
| - Accuracy: 0.9748 |
| - F1: 0.9565 |
| - Precision: 0.9429 |
| - Recall: 0.9706 |
|
|
| ## 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: 16 |
| - 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 |
| - num_epochs: 5 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 0.0611 | 1.0 | 90 | 0.1527 | 0.9552 | 0.9245 | 0.8909 | 0.9608 | |
| | 0.0686 | 2.0 | 180 | 0.1356 | 0.9636 | 0.9378 | 0.9159 | 0.9608 | |
| | 0.0052 | 3.0 | 270 | 0.1308 | 0.9748 | 0.9565 | 0.9429 | 0.9706 | |
| | 0.0206 | 4.0 | 360 | 0.1425 | 0.9636 | 0.9372 | 0.9238 | 0.9510 | |
| | 0.0049 | 5.0 | 450 | 0.1565 | 0.9692 | 0.9458 | 0.9505 | 0.9412 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.7.0 |
| - Pytorch 2.11.0+cu130 |
| - Datasets 4.8.5 |
| - Tokenizers 0.22.2 |
| |
