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README.md

@@ -1,61 +1,174 @@
 
 ---
 license: apache-2.0
+library_name: transformers
+pipeline_tag: text-classification
 tags:
+- transformers
 - text-classification
-- sms-spam
 - distilbert
+- sms-spam
 - mlops
+- kaggle
+- wandb
+- huggingface
 - iit-jodhpur
 - group-36
 - G25AIT2016
 datasets:
 - ucirvine/sms_spam
 base_model: distilbert-base-uncased
-pipeline_tag: text-classification
+language:
+- en
 ---
 
-# MLOPS_group-v4
-
-This Hugging Face repository is part of the MLOps Group 36 project for the PGD AI Programme, IIT Jodhpur.
+# MLOPS_group-v4 — SMS Spam Classification
 
-## Project
+This repository is part of the **MLOps Group 36 Project** for the **PGD AI Programme, IIT Jodhpur**.
 
-SMS Spam Classification using DistilBERT.
+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
+**Anu Kumar**  
+Roll Number: **G25AIT2016**
 
-## Contribution
+## Project Links
 
-This version is linked to the Task 2 data preparation workflow completed by G25AIT2016.
+| 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 |
 
-The data preparation workflow included:
+## Model Details
 
-- Loading the UCI SMS Spam Collection dataset
-- Text cleaning and normalisation
-- Duplicate removal
-- Stratified train, validation, and test split
-- Label mapping for ham and spam classes
-- Sanity checks
-- Data versioning and traceability
-- W&B run logging for data-preparation metrics
+| 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 |
 
-## Links
+## Contribution Summary
 
-Kaggle Notebook: https://www.kaggle.com/code/anukumarkg25ait2016/mlops-group36-data-preprocessing-g25ait2016
+This repository is linked to the **G25AIT2016 Task 2 workflow**.
 
-W&B Run: https://wandb.ai/g25ait2032-iit-jodhpur/MLOPS_Group/runs/j5fk4zll
+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
 
-GitHub Repository: https://github.com/g25ait2032-prog/mlops-group36-iitj
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-## Labels
+tokenizer = AutoTokenizer.from_pretrained("Aukrk/MLOPS_group-v4")
+model = AutoModelForSequenceClassification.from_pretrained("Aukrk/MLOPS_group-v4")
+```
 
-- 0: ham
-- 1: spam
+## 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
 
-The model artefact is based on the Group 36 DistilBERT SMS spam classification workflow and is published under the G25AIT2016 Hugging Face account for project traceability.
+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**