---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scam-alert-bert
  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. -->

# scam-alert-bert

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: 0.1215
- Accuracy: 0.9861
- F1: 0.9861

## 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: 8
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.1577 | 100  | 0.0723          | 0.9841   | 0.9841 |
| No log        | 0.3155 | 200  | 0.0520          | 0.9900   | 0.9899 |
| No log        | 0.4732 | 300  | 0.0825          | 0.9821   | 0.9823 |
| No log        | 0.6309 | 400  | 0.0722          | 0.9861   | 0.9857 |
| No log        | 0.7886 | 500  | 0.0521          | 0.9861   | 0.9859 |
| No log        | 0.9464 | 600  | 0.0905          | 0.9761   | 0.9765 |
| No log        | 1.1041 | 700  | 0.0675          | 0.9821   | 0.9822 |
| No log        | 1.2618 | 800  | 0.0661          | 0.9900   | 0.9899 |
| No log        | 1.4196 | 900  | 0.0695          | 0.9861   | 0.9861 |
| No log        | 1.5773 | 1000 | 0.0780          | 0.9880   | 0.9880 |
| No log        | 1.7350 | 1100 | 0.0877          | 0.9861   | 0.9858 |
| No log        | 1.8927 | 1200 | 0.0714          | 0.9880   | 0.9880 |
| No log        | 2.0505 | 1300 | 0.0856          | 0.9841   | 0.9841 |
| No log        | 2.2082 | 1400 | 0.0930          | 0.9841   | 0.9842 |
| No log        | 2.3659 | 1500 | 0.0886          | 0.9861   | 0.9860 |
| No log        | 2.5237 | 1600 | 0.0982          | 0.9861   | 0.9861 |
| No log        | 2.6814 | 1700 | 0.0854          | 0.9861   | 0.9860 |
| No log        | 2.8391 | 1800 | 0.0949          | 0.9861   | 0.9860 |
| No log        | 2.9968 | 1900 | 0.0935          | 0.9880   | 0.9880 |
| No log        | 3.1546 | 2000 | 0.1004          | 0.9861   | 0.9860 |
| No log        | 3.3123 | 2100 | 0.1223          | 0.9801   | 0.9803 |
| No log        | 3.4700 | 2200 | 0.1146          | 0.9861   | 0.9861 |
| No log        | 3.6278 | 2300 | 0.1148          | 0.9861   | 0.9861 |
| No log        | 3.7855 | 2400 | 0.1074          | 0.9861   | 0.9860 |
| No log        | 3.9432 | 2500 | 0.1082          | 0.9861   | 0.9860 |
| 0.0249        | 4.1009 | 2600 | 0.1272          | 0.9841   | 0.9842 |
| 0.0249        | 4.2587 | 2700 | 0.1264          | 0.9841   | 0.9842 |
| 0.0249        | 4.4164 | 2800 | 0.1161          | 0.9861   | 0.9861 |
| 0.0249        | 4.5741 | 2900 | 0.1242          | 0.9861   | 0.9861 |
| 0.0249        | 4.7319 | 3000 | 0.1227          | 0.9861   | 0.9861 |
| 0.0249        | 4.8896 | 3100 | 0.1223          | 0.9861   | 0.9861 |
| 0.0249        | 5.0473 | 3200 | 0.1207          | 0.9861   | 0.9861 |
| 0.0249        | 5.2050 | 3300 | 0.1210          | 0.9861   | 0.9861 |
| 0.0249        | 5.3628 | 3400 | 0.1214          | 0.9861   | 0.9861 |
| 0.0249        | 5.5205 | 3500 | 0.1214          | 0.9861   | 0.9861 |
| 0.0249        | 5.6782 | 3600 | 0.1215          | 0.9861   | 0.9861 |
| 0.0249        | 5.8360 | 3700 | 0.1215          | 0.9861   | 0.9861 |
| 0.0249        | 5.9937 | 3800 | 0.1215          | 0.9861   | 0.9861 |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1