---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert-agent-scam-classifier-v1.0
  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. -->

# bert-agent-scam-classifier-v1.0

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co./google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0039
- Accuracy: {'accuracy': 0.996875}
- Precision: {'precision': 0.996894409937888}
- Recall: {'recall': 0.996875}
- F1: {'f1': 0.9968749694821237}

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy               | Precision                        | Recall               | F1                         |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:--------------------------------:|:--------------------:|:--------------------------:|
| No log        | 1.0   | 160  | 0.0032          | {'accuracy': 0.996875} | {'precision': 0.996894409937888} | {'recall': 0.996875} | {'f1': 0.9968749694821237} |
| No log        | 2.0   | 320  | 0.0033          | {'accuracy': 0.996875} | {'precision': 0.996894409937888} | {'recall': 0.996875} | {'f1': 0.9968749694821237} |
| No log        | 3.0   | 480  | 0.0034          | {'accuracy': 0.996875} | {'precision': 0.996894409937888} | {'recall': 0.996875} | {'f1': 0.9968749694821237} |
| 0.0354        | 4.0   | 640  | 0.0034          | {'accuracy': 0.996875} | {'precision': 0.996894409937888} | {'recall': 0.996875} | {'f1': 0.9968749694821237} |
| 0.0354        | 5.0   | 800  | 0.0039          | {'accuracy': 0.996875} | {'precision': 0.996894409937888} | {'recall': 0.996875} | {'f1': 0.9968749694821237} |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1