---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: google-bert/bert-base-uncased
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-finetuned-spam
  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-finetuned-spam

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: 0.1942
- Accuracy: 0.952
- F1: 0.9502
- Precision: 0.9871
- Recall: 0.916

## 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: 2.5719605731158755e-06
- train_batch_size: 4
- eval_batch_size: 2
- seed: 19
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2449        | 1.0   | 2250  | 0.2435          | 0.901    | 0.8930 | 0.9718    | 0.826  |
| 0.2545        | 2.0   | 4500  | 0.2138          | 0.937    | 0.9336 | 0.9866    | 0.886  |
| 0.1397        | 3.0   | 6750  | 0.2162          | 0.944    | 0.9413 | 0.9890    | 0.898  |
| 0.1184        | 4.0   | 9000  | 0.2134          | 0.946    | 0.9436 | 0.9869    | 0.904  |
| 0.2056        | 5.0   | 11250 | 0.1942          | 0.952    | 0.9502 | 0.9871    | 0.916  |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1