---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: HamSpamBERT
  results: []
widget:
- text: "Ok i am on the way to home bye"
  example_title: "Ham"
- text: "PRIVATE! Your 2004 Account Statement for 07742676969 shows 786 unredeemed Bonus Points. To claim call 08719180248 Identifier Code: 45239 Expires"
  example_title: "Spam"
---

<!-- 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. -->

# HamSpamBERT

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on [Spam-Ham](https://huggingface.co/datasets/SalehAhmad/Spam-Ham) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0072
- Accuracy: 0.9991
- Precision: 1.0
- Recall: 0.9933
- F1: 0.9966

```python
from transformers import pipeline, BertTokenizer, BertForSequenceClassification

tokenizer = BertTokenizer.from_pretrained("udit-k/HamSpamBERT")
model = BertForSequenceClassification.from_pretrained("udit-k/HamSpamBERT")

classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
print(classifier("Call this number to win FREE IPL FINAL tickets!!!"))
print(classifier("Call me when you reach home :)"))
```
```
[{'label': 'LABEL_1', 'score': 0.9999189376831055}]
[{'label': 'LABEL_0', 'score': 0.9999370574951172}]
```

## Model description

This model is a fine-tuned version of the [BERT](https://huggingface.co/bert-base-uncased) model on [Spam-Ham](https://huggingface.co/datasets/SalehAhmad/Spam-Ham) dataset to improve the performance of sentiment analysis on Spam Detection tasks.
- LABEL_0 = Ham (Not spam)
-  LABEL_1 = Spam

## Intended uses & limitations

This model can be used to detect spam texts. The primary limitation of this model is that it was trained on a corpus of about 4700 rows and evaluated on around 1200 rows.

## Training and evaluation data

- Training corpus = 80%
- Evaluation corpus = 20%


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 279  | 0.0492          | 0.9901   | 1.0       | 0.9262 | 0.9617 |
| 0.0635        | 2.0   | 558  | 0.0117          | 0.9982   | 1.0       | 0.9866 | 0.9932 |
| 0.0635        | 3.0   | 837  | 0.0120          | 0.9982   | 0.9933    | 0.9933 | 0.9933 |
| 0.0138        | 4.0   | 1116 | 0.0072          | 0.9991   | 1.0       | 0.9933 | 0.9966 |
| 0.0138        | 5.0   | 1395 | 0.0086          | 0.9982   | 0.9933    | 0.9933 | 0.9933 |
| 0.0007        | 6.0   | 1674 | 0.0090          | 0.9982   | 0.9933    | 0.9933 | 0.9933 |
| 0.0007        | 7.0   | 1953 | 0.0091          | 0.9982   | 0.9933    | 0.9933 | 0.9933 |


### Framework versions

- Transformers 4.30.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.13.3