---
library_name: transformers
tags:
  - text-classification
  - spam-detection
  - distilbert
  - sms
  - pytorch
language:
  - en
license: mit
datasets:
  - sms_spam
metrics:
  - accuracy
  - f1
pipeline_tag: text-classification
---

# SMS Spam Classifier — DistilBERT (Group 36, IIT Jodhpur)

Fine-tuned `distilbert-base-uncased` for binary SMS spam classification. Achieves **99.35% accuracy** and **0.9851 F1 Macro** on the held-out test set. This is **v2** — the best-performing version by validation loss (0.0292).

Developed as part of the MLOps course, PGD AI Program, IIT Jodhpur.

---

## Model Details

### Model Description

- **Base model:** `distilbert-base-uncased` (66M parameters)
- **Task:** Binary text classification — Ham (0) vs Spam (1)
- **Dataset:** UCI SMS Spam Collection (5,159 samples after deduplication)
- **Architecture:** DistilBERT encoder + linear classification head
- **Framework:** PyTorch + Hugging Face Transformers
- **Training platform:** Kaggle (NVIDIA T4 x2 GPU)
- **Developed by:** MLOps Group 36, IIT Jodhpur
- **Model card authors:** G25AIT2032 Duggirala Vnaga Ananth
- **Contact:** g25ait2032@iitj.ac.in

### Related Resources

| Resource | Link |
|---|---|
| GitHub Repository | [MLOps Group 36 Repository](https://github.com/g25ait2032-prog/mlops-group36-iitj) |
| Kaggle Notebook (Final) | [mlops-group36-final-v3](https://www.kaggle.com/code/g25ait2032/mlops-group36-final-v3) |
| W&B Dashboard | [MLOPS_Group](https://wandb.ai/g25ait2032-iit-jodhpur/MLOPS_Group) |
| HF Model — v1 | [nagaananth/MLOPS_group-v1](https://huggingface.co/nagaananth/MLOPS_group-v1) |
| HF Model — v2 ★ Best | [nagaananth/MLOPS_group-v2](https://huggingface.co/nagaananth/MLOPS_group-v2) |
| HF Model — v3 | [nagaananth/MLOPS_group-v3](https://huggingface.co/nagaananth/MLOPS_group-v3) |
| HF Model — v4 | [nagaananth/MLOPS_group-v4](https://huggingface.co/nagaananth/MLOPS_group-v4) |
| Docker Image (GHCR) | `ghcr.io/g25ait2032-prog/mlops_group-inference:latest` |
| Docker Image (Hub) | `dvnananth/mlops-group36:v1` |

---

## How to Get Started

```python
from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="nagaananth/MLOPS_group-v2"
)

# Spam example
print(classifier("URGENT! You have won a free iPhone. Click here now."))
# [{'label': 'spam', 'score': 0.9804}]

# Ham example
print(classifier("Hey, are we still meeting for lunch at 12?"))
# [{'label': 'ham', 'score': 0.9982}]
```

Or with full control:

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "nagaananth/MLOPS_group-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
    with torch.no_grad():
        logits = model(**inputs).logits
    probs = torch.softmax(logits, dim=-1)[0]
    pred_idx = probs.argmax().item()
    return {
        "label": model.config.id2label[pred_idx],
        "score": round(probs[pred_idx].item(), 4)
    }

print(predict("Free prize! Click now to claim your reward."))
# {'label': 'spam', 'score': 0.9897}
```

---

## Training Details

### Dataset

**UCI SMS Spam Collection** loaded via HuggingFace `datasets` (`sms_spam`).

| Split | Samples | Ham % | Spam % |
|---|---|---|---|
| Train (70%) | 3,611 | ~87.5 | ~12.5 |
| Validation (15%) | 774 | ~87.5 | ~12.5 |
| Test (15%) | 774 | ~87.5 | ~12.5 |

**Preprocessing steps:**
- Lowercased and whitespace normalised
- 415 duplicate messages removed (total: 5,159 unique samples)
- Stratified 70/15/15 split with zero-leakage verification
- Tokenized with `AutoTokenizer` for DistilBERT (`truncation=True, max_length=128`)
- Labels mapped: `{"ham": 0, "spam": 1}`

### Hyperparameter Comparison (All Versions)

| Version | LR | Epochs | Batch Size | Warmup | Weight Decay | Early Stopping | Val Loss | F1 Macro |
|---|---|---|---|---|---|---|---|---|
| v1 | 3e-5 | 3 | 16 | 100 | 0.01 | No | 0.0539 | 0.9849 |
| v2 ★ | 2e-5 | 5 | 32 | 200 | 0.01 | Yes (p=2) | **0.0292** | **0.9851** |
| v3 | 2e-5 | 5 | 32 | 200 | 0.01 | Yes (p=2) | 0.0376 | 0.9851 |
| v4 | 1e-5 | 4 | 16 | 200 | 0.02 | Yes (p=2) | — | — |

**v2** was selected as the final deployment model due to its lowest validation loss (0.0292), indicating the best generalisation.

### Training Configuration (v2)

- **Optimizer:** AdamW
- **Learning rate:** 2e-5
- **Epochs:** 5 (with early stopping, patience=2)
- **Batch size:** 32 (train), 64 (eval)
- **Mixed precision:** fp16
- **Metric for best model:** F1 Weighted
- **Infrastructure:** Kaggle NVIDIA T4 x2 GPU
- **Average training time:** ~2 minutes per run

---

## Evaluation Results

### Test Set Performance (v2 — Best Model)

| Metric | Score |
|---|---|
| Accuracy | 0.9935 |
| F1 Weighted | 0.9935 |
| F1 Macro | 0.9851 |
| Precision | 0.9935 |
| Recall | 0.9935 |
| Validation Loss | 0.0292 |

### Adversarial Test Cases

The model was evaluated on 15 adversarial/edge-case SMS messages covering spam, ham, and ambiguous phrasing (e.g., messages mixing casual language with spam triggers). Representative examples:

| Text | True | Predicted | Confidence |
|---|---|---|---|
| "URGENT! You have won a 1-week cruise! Call now." | spam | spam | 0.9987 |
| "You won! Click here to claim your prize." | spam | spam | 0.9945 |
| "Hey, are we still meeting for lunch at 12?" | ham | ham | 0.9991 |
| "Can you send me the report by EOD?" | ham | ham | 0.9988 |
| "Meeting for lunch? I won a contest, let's talk." | ham | ham | 0.9756 |

### Inference Latency (CPU)

- Mean latency: ~30–60 ms per sample
- Suitable for CPU-only deployment

---

## Uses

### Direct Use

Binary classification of SMS or short-text messages into `ham` (legitimate) or `spam` (unsolicited/phishing). Can be directly integrated into messaging applications or notification pipelines.

### Downstream Use

Can serve as a component in broader security pipelines for filtering suspicious incoming messages, or as a baseline for transfer learning to other spam-detection domains.

### Out-of-Scope Use

- Long-form document classification
- Sentiment analysis or intent detection
- Legal or financial decision-making without human oversight
- Languages other than English

---

## Bias, Risks, and Limitations

**Data Bias:** Trained on a specific SMS corpus from the early 2010s. May struggle with modern slang, emojis, or evolved phishing techniques not present in the training data.

**False Positives:** Messages containing spam-adjacent keywords (e.g., "Urgent", "Click", "Won") in legitimate contexts may be misclassified.

**Contextual Blindness:** Processes each message independently; cannot use conversational context from prior messages.

**Phishing Sophistication:** Less reliable against highly sophisticated spear-phishing that mimics professional language.

### Recommendations

- Notify users when a message is flagged automatically.
- Provide a manual override/report mechanism for misclassifications.
- Monitor for distribution drift and retrain periodically on newer data.

---

## Technical Specifications

### Model Architecture

- **Base:** `distilbert-base-uncased` (6 transformer layers, 768 hidden dim, 12 attention heads)
- **Classification head:** Linear layer over `[CLS]` token pooled output → 2 classes
- **Total parameters:** ~66M

### Compute Infrastructure

- **Training:** Kaggle Notebooks — NVIDIA T4 x2 GPU
- **Libraries:** `transformers`, `datasets`, `evaluate`, `accelerate`, `torch`, `wandb`
- **Inference:** CPU-compatible (no GPU required)

### Environmental Impact

- **Hardware:** NVIDIA T4 GPU (Kaggle)
- **Training duration:** ~2 minutes per run
- **Carbon emitted:** < 0.01 kg CO₂eq (estimated via ML Impact Calculator)

---

## Citation

```bibtex
@misc{group36-sms-spam-2026,
  author    = {Duggirala Vnaga Ananth and Anukumar K and Shrikrishna Tripathi and Sudeb Ghosh},
  title     = {SMS Spam Classifier: Fine-tuned DistilBERT (Group 36, IIT Jodhpur)},
  year      = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/nagaananth/MLOPS_group-v2}}
}
```

---

## Glossary

- **Ham:** Legitimate, non-spam SMS message
- **Spam:** Unsolicited commercial or phishing message
- **DistilBERT:** Distilled version of BERT — 40% smaller, retains 97% of BERT's NLU performance
- **F1 Macro:** Unweighted mean of per-class F1 scores; useful for evaluating imbalanced datasets
- **Fine-tuning:** Adapting a pre-trained language model to a task-specific dataset with supervised training