---
license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
tags:
- transformers
- text-classification
- distilbert
- sms-spam
- mlops
- kaggle
- wandb
- huggingface
- iit-jodhpur
- group-36
- G25AIT2016
datasets:
- ucirvine/sms_spam
base_model: distilbert-base-uncased
language:
- en
---

# MLOPS_group-v4 — SMS Spam Classification

This repository is part of the **MLOps Group 36 Project** for the **PGD AI Programme, IIT Jodhpur**.

The project implements an end-to-end MLOps pipeline for SMS spam classification using **DistilBERT**, with GitHub, Kaggle, Weights & Biases, Hugging Face Hub, Docker, and GitHub Actions.

## Contributor

**Anu Kumar**  
Roll Number: **G25AIT2016**

## Project Links

| Resource | Link |
|---|---|
| GitHub Repository | https://github.com/g25ait2032-prog/mlops-group36-iitj |
| Kaggle Notebook - G25AIT2016 | https://www.kaggle.com/code/anukumarkg25ait2016/mlops-group36-data-preprocessing-g25ait2016 |
| W&B Run - G25AIT2016 | https://wandb.ai/g25ait2032-iit-jodhpur/MLOPS_Group/runs/j5fk4zll |
| W&B Project Dashboard | https://wandb.ai/g25ait2032-iit-jodhpur/MLOPS_Group |
| Hugging Face Model | https://huggingface.co/Aukrk/MLOPS_group-v4 |

## Model Details

| Item | Value |
|---|---|
| Base model | distilbert-base-uncased |
| Task | Binary text classification |
| Classes | ham, spam |
| Dataset | UCI SMS Spam Collection |
| Framework | Hugging Face Transformers |
| Output labels | 0 = ham, 1 = spam |

## Contribution Summary

This repository is linked to the **G25AIT2016 Task 2 workflow**.

The completed contribution includes:

- Loading the UCI SMS Spam dataset
- Cleaning and normalising SMS text
- Removing missing and duplicate records
- Creating stratified train, validation, and test splits
- Creating `id2label.json` and `label2id.json`
- Running data sanity checks
- Logging data-preparation metrics to W&B
- Publishing this Hugging Face model repository for project traceability

## Dataset Preparation Summary

| Metric | Value |
|---|---:|
| Raw samples | 5,574 |
| Duplicates removed | 415 |
| Cleaned samples | 5,159 |
| Train rows | 3,611 |
| Validation rows | 774 |
| Test rows | 774 |
| Sanity checks passed | 21 / 21 |
| Leakage check | Passed |

## Label Mapping

```json
{
  "0": "ham",
  "1": "spam"
}
```

## How to Use

```python
from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Aukrk/MLOPS_group-v4"
)

text = "Congratulations! You have won a free iPhone. Click here now."
result = classifier(text)

print(result)
```

## Load Model Directly

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Aukrk/MLOPS_group-v4")
model = AutoModelForSequenceClassification.from_pretrained("Aukrk/MLOPS_group-v4")
```

## Example Inputs

| Text | Expected Output |
|---|---|
| Congratulations! You have won a free prize. Click here now. | spam |
| Can we meet tomorrow at 5 PM? | ham |

## W&B Traceability

The G25AIT2016 W&B run records data-preparation metrics such as:

- Raw sample count
- Duplicate removal count
- Cleaned sample count
- Train / validation / test split sizes
- Sanity check status
- Leakage check status

W&B Run: https://wandb.ai/g25ait2032-iit-jodhpur/MLOPS_Group/runs/j5fk4zll

## Model Context

This model repository is published under the G25AIT2016 Hugging Face account for Group 36 project traceability.

The model artefact follows the Group 36 DistilBERT SMS spam classification workflow and is linked with the data-preparation contribution completed by Anu Kumar - G25AIT2016.

## Limitations

- The dataset is relatively small and focused on SMS messages.
- The model may not generalise well to long emails, non-English messages, or modern scam formats.
- Boundary cases mixing normal conversation and promotional text may be misclassified.
- This is an academic MLOps demonstration and should not be used as the only spam detection control in production.

## Intended Use

This repository is intended for:

- Academic MLOps demonstration
- SMS spam classification testing
- Hugging Face deployment evidence
- W&B traceability evidence
- GitHub Actions / Docker inference integration

## Not Intended For

- Production-grade fraud detection
- Legal, financial, or safety-critical filtering
- Detecting all phishing or scam variants without further validation

## Authors

MLOps Group 36  
PGD AI Programme, IIT Jodhpur

Contributor for this repository:  
**Anu Kumar - G25AIT2016**