license: apache-2.0 | |
tags: | |
- generated_from_trainer | |
metrics: | |
- accuracy | |
- recall | |
- precision | |
model-index: | |
- name: police-lethal-force-classifier | |
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. --> | |
# police-lethal-force-classifier | |
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/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 | |