---
license: apache-2.0
base_model: google-bert/bert-base-uncased
metrics:
- accuracy
model-index:
- name: results
  results: []
pipeline_tag: text-classification
library_name: transformers
widget:
  - inference: true
  - PipelineType: text-classification
---

Hi, developer here !! Please do download and try out the model locally or on colab, as it helps huggingface determine that this model is important enough to have a serverless API for everyone to use. Thank You !!

# Overview

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on this [Kaggle](https://www.kaggle.com/code/djonafegnem/chicago-crime-data-analysis) dataset.
It achieves the following results on the evaluation set:
<!-- - Loss: 12.41% -->
- Macro f1: 89.44%
- Weighted f1: 93.15%
- Accuracy: 93.80%
- Balanced accuracy: 90.42%

## Model description

This finetuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) excels at detecting the crime type from the description of the crime. It has 34 labels.


## Training and evaluation data

<!-- - eval_loss: 12.41% -->
- eval_macro f1: 89.44%
- eval_weighted f1: 93.15%
- eval_accuracy: 93.79%
- eval_balanced accuracy: 90.42%


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
| 0.1859        | 1.0   | 5538   | 0.1297          | 0.8561   | 0.9249      | 0.9366   | 0.8571            |
| 0.1281        | 2.0   | 11076  | 0.1260          | 0.8702   | 0.9248      | 0.9369   | 0.8740            |
| 0.1279        | 3.0   | 16614  | 0.1251          | 0.8728   | 0.9314      | 0.9380   | 0.8749            |
| 0.1272        | 4.0   | 22152  | 0.1276          | 0.8652   | 0.9247      | 0.9367   | 0.8655            |
| 0.1266        | 5.0   | 27690  | 0.1256          | 0.8685   | 0.9252      | 0.9345   | 0.8724            |
| 0.1284        | 6.0   | 33228  | 0.1264          | 0.8668   | 0.9252      | 0.9345   | 0.8724            |
| 0.1272        | 7.0   | 38766  | 0.1247          | 0.8739   | 0.9313      | 0.9379   | 0.8748            |
| 0.1262        | 8.0   | 44304  | 0.1258          | 0.8892   | 0.9246      | 0.9366   | 0.9024            |
| 0.1263        | 9.0   | 49842  | 0.1251          | 0.9038   | 0.9310      | 0.9378   | 0.9041            |
| 0.1267        | 10.0  | 55380  | 0.1244          | 0.8897   | 0.9253      | 0.9345   | 0.9018            |
| 0.1271        | 11.0  | 60918  | 0.1251          | 0.8951   | 0.9325      | 0.9371   | 0.9036            |
| 0.1268        | 12.0  | 66456  | 0.1248          | 0.8944   | 0.9315      | 0.9380   | 0.9042            |
| 0.1254        | 13.0  | 71994  | 0.1247          | 0.9038   | 0.9314      | 0.9381   | 0.9043            |
| 0.126         | 14.0  | 77532  | 0.1263          | 0.8944   | 0.9314      | 0.9379   | 0.9042            |
| 0.1261        | 15.0  | 83070  | 0.1274          | 0.8891   | 0.9250      | 0.9348   | 0.9020            |
| 0.1253        | 16.0  | 88608  | 0.1241          | 0.8944   | 0.9315      | 0.9380   | 0.9042            |
| 0.1251        | 17.0  | 94146  | 0.1244          | 0.9042   | 0.9314      | 0.9380   | 0.9042            |
| 0.125         | 18.0  | 99684  | 0.1249          | 0.9041   | 0.9314      | 0.9380   | 0.9043            |
| 0.125         | 19.0  | 105222 | 0.1245          | 0.8942   | 0.9312      | 0.9380   | 0.9042            |
| 0.1257        | 20.0  | 110760 | 0.1248          | 0.9041   | 0.9313      | 0.9379   | 0.9042            |
| 0.125         | 21.0  | 116298 | 0.1248          | 0.9000   | 0.9254      | 0.9344   | 0.9018            |
| 0.1248        | 22.0  | 121836 | 0.1244          | 0.9041   | 0.9313      | 0.9379   | 0.9042            |
| 0.1246        | 23.0  | 127374 | 0.1245          | 0.9042   | 0.9315      | 0.9380   | 0.9042            |
| 0.1247        | 24.0  | 132912 | 0.1242          | 0.8943   | 0.9314      | 0.9380   | 0.9043            |
| 0.1245        | 25.0  | 138450 | 0.1242          | 0.9042   | 0.9315      | 0.9380   | 0.9042            |
| 0.1245        | 26.0  | 143988 | 0.1245          | 0.9042   | 0.9314      | 0.9381   | 0.9043            |
| 0.1245        | 27.0  | 149526 | 0.1242          | 0.8944   | 0.9314      | 0.9381   | 0.9043            |
| 0.1244        | 28.0  | 155064 | 0.1242          | 0.9336   | 0.9315      | 0.9381   | 0.9337            |
| 0.1243        | 29.0  | 160602 | 0.1243          | 0.8944   | 0.9314      | 0.9381   | 0.9043            |
| 0.1243        | 30.0  | 166140 | 0.1243          | 0.8944   | 0.9314      | 0.9381   | 0.9043            |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2