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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
|