---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: spam-detection_m1
  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. -->

# spam-detection_m1

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co./google-bert/bert-base-uncased) on an [spam-detection](https://huggingface.co./datasets/vishnun0027/spam-detection) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0202
- Accuracy: 0.9967

## 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: 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 256  | 0.1144          | 0.9919   |
| 0.22          | 2.0   | 512  | 0.0483          | 0.9923   |
| 0.22          | 3.0   | 768  | 0.0321          | 0.9949   |
| 0.0361        | 4.0   | 1024 | 0.0275          | 0.9949   |
| 0.0361        | 5.0   | 1280 | 0.0245          | 0.9952   |
| 0.0233        | 6.0   | 1536 | 0.0232          | 0.9960   |
| 0.0233        | 7.0   | 1792 | 0.0220          | 0.9967   |
| 0.0171        | 8.0   | 2048 | 0.0209          | 0.9967   |
| 0.0171        | 9.0   | 2304 | 0.0211          | 0.9967   |
| 0.0148        | 10.0  | 2560 | 0.0202          | 0.9967   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0