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# email_spam_classification_eng

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0483
- Accuracy: 0.9928
- Precision: 0.9928
- Recall: 0.9928
- F1 Score: 0.9928

## 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: 3e-06
- 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 0.0073        | 1.0   | 592  | 0.0707          | 0.9916   | 0.9916    | 0.9916 | 0.9915   |
| 0.0043        | 2.0   | 1184 | 0.0679          | 0.9904   | 0.9904    | 0.9904 | 0.9904   |
| 0.0022        | 3.0   | 1776 | 0.0564          | 0.9916   | 0.9916    | 0.9916 | 0.9916   |
| 0.0015        | 4.0   | 2368 | 0.0483          | 0.9928   | 0.9928    | 0.9928 | 0.9928   |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "base_model": "bert-base-uncased", "model-index": [{"name": "email_spam_classification_eng", "results": []}]}
transformers safetensors bert text-classification generated_from_trainer base_model:bert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us