---
license: apache-2.0
lang:
- en
tags:
- generated_from_trainer
- spam
- spam detection
metrics:
- precision
- recall
- accuracy
- f1
datasets:
- SetFit/enron_spam
model-index:
- name: bert-tiny-finetuned-enron-spam-detection
  results: []
  
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---

<!-- 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-Tiny fine-tuned on Enron Spam Detection

This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) (aka BERT-Tiny) on an [SetFit/enron_spam](https://huggingface.co/datasets/SetFit/enron_spam) for `Spam Dectection` downstream task.


It achieves the following results on the evaluation set:
- Loss: 0.0593
- Precision: 0.9851
- Recall: 0.9871
- Accuracy: 0.986
- 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: 16
- eval_batch_size: 32
- 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 | Precision | Recall | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|
| 0.1125        | 1.0   | 1983 | 0.0797          | 0.9839    | 0.9692 | 0.9765   | 0.9765 |
| 0.061         | 2.0   | 3966 | 0.0618          | 0.9822    | 0.9861 | 0.984    | 0.9842 |
| 0.0486        | 3.0   | 5949 | 0.0593          | 0.9851    | 0.9871 | 0.986    | 0.9861 |
| 0.048         | 4.0   | 7932 | 0.0588          | 0.9870    | 0.9821 | 0.9845   | 0.9846 |


### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1