| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: jim-crow-test2323 |
| 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-test2323 |
|
|
| 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: 0.0984 |
| - Accuracy: 0.9720 |
| - Precision: 0.9340 |
| - Recall: 0.9706 |
| - F1: 0.9519 |
| - Macro Precision: 0.9610 |
| - Macro Recall: 0.9716 |
| - Macro F1: 0.9661 |
| - Tn: 248 |
| - Fp: 7 |
| - Fn: 3 |
| - Tp: 99 |
|
|
| ## 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 | Precision | Recall | F1 | Macro Precision | Macro Recall | Macro F1 | Tn | Fp | Fn | Tp | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:---------------:|:------------:|:--------:|:---:|:--:|:--:|:---:| |
| | 0.0677 | 1.0 | 90 | 0.1643 | 0.9524 | 0.8899 | 0.9510 | 0.9194 | 0.9349 | 0.9520 | 0.9428 | 243 | 12 | 5 | 97 | |
| | 0.1282 | 2.0 | 180 | 0.0984 | 0.9720 | 0.9340 | 0.9706 | 0.9519 | 0.9610 | 0.9716 | 0.9661 | 248 | 7 | 3 | 99 | |
| | 0.0683 | 3.0 | 270 | 0.1819 | 0.9720 | 0.9694 | 0.9314 | 0.95 | 0.9712 | 0.9598 | 0.9653 | 252 | 3 | 7 | 95 | |
| | 0.0226 | 4.0 | 360 | 0.1095 | 0.9692 | 0.9174 | 0.9804 | 0.9479 | 0.9547 | 0.9725 | 0.9630 | 246 | 9 | 2 | 100 | |
| | 0.0219 | 5.0 | 450 | 0.1491 | 0.9720 | 0.9423 | 0.9608 | 0.9515 | 0.9632 | 0.9686 | 0.9659 | 249 | 6 | 4 | 98 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 5.7.0 |
| - Pytorch 2.11.0+cu130 |
| - Datasets 4.8.5 |
| - Tokenizers 0.22.2 |
|
|
