<!-- 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. -->
police-lethal-force-classifier
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0087
- Accuracy: 0.9980
- F1-score: 0.9964
- Recall: 0.9965
- Precision: 0.9963
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Recall | Precision |
|---|---|---|---|---|---|---|---|
| 0.0138 | 1.0 | 12050 | 0.0132 | 0.9973 | 0.9951 | 0.9953 | 0.9949 |
| 0.0091 | 2.0 | 24100 | 0.0087 | 0.9980 | 0.9964 | 0.9965 | 0.9963 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
