---
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