Update README.md

README.md

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   - split: train
     path: data/train-*
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+
+# SMS Spam Enriched Dataset
+
+An enriched version of the classic **SMS Spam Collection Dataset from UC Irvine** with additional engineered features and semantic embeddings.  
+This dataset is designed for **spam detection, feature engineering experiments, and model interpretability research**.
+
+---
+
+## Dataset Overview
+
+- **Total samples**: 5,171  
+- **Classes**: 
+  - `0`: Ham (non-spam)  
+  - `1`: Spam  
+
+---
+
+## Enrichments Added
+
+Alongside the raw SMS text (`sms`) and labels (`label`), we engineered multiple new features:
+
+1. **char_len** → Total characters in the message  
+2. **word_count** → Total words in the message  
+3. **punct_score** → Weighted score for punctuation usage (`!`, `?`, `...`)  
+4. **spam_keywords** → Count of known spammy tokens (`free`, `win`, `urgent`, etc.)  
+5. **lexical_diversity** → Ratio of unique words to total words  
+6. **readability** → Flesch Reading Ease score  
+7. **caps_ratio** → Proportion of uppercase characters  
+8. **digit_ratio** → Proportion of numeric digits  
+9. **exclaim_ratio** → Ratio of exclamation marks to total characters  
+10. **url_flag** → Binary indicator for presence of URLs  
+11. **spammy_words** → Count of flagged high-signal words  
+12. **entropy** → Shannon entropy of characters  
+13. **embeddings** → Sentence-transformer vector representations (for downstream tasks like clustering, semantic similarity, visualization)
+
+---
+
+## Example Row
+
+| sms                                            | label | char_len | word_count | punct_score | spam_keywords | lexical_diversity | readability | caps_ratio | digit_ratio | url_flag | entropy |
+|------------------------------------------------|-------|----------|------------|-------------|---------------|-------------------|-------------|------------|-------------|----------|---------|
+| "Free entry in 2 a wkly comp to win FA Cup..." |   1   |   156    |    28      |     0       |      3        |       0.857       |    80.83    |   0.064    |   0.16      |    0     |  4.69   |
+
+---
+
+## Visualizations
+
+Below is a **PCA projection** of SMS embeddings, showing clear separation between spam (red) and ham (blue):  
+
+![PCA Plot](PCA project of sms embeddings.png)
+
+---
+
+## Benchmark Models
+
+We trained baseline classifiers using the enriched dataset:
+
+- **Logistic Regression (with combined features)**  
+  - Accuracy: ~99%  
+  - F1 (spam): ~0.95  
+
+- **Random Forest (with combined features)**  
+  - Accuracy: ~98%  
+  - F1 (spam): ~0.92
+
+  Logistic Regression Report (Combined Features):
+              precision    recall  f1-score   support
+
+           0       0.99      1.00      0.99       904
+           1       0.98      0.92      0.95       131
+
+    accuracy                           0.99      1035
+   macro avg       0.99      0.96      0.97      1035
+weighted avg       0.99      0.99      0.99      1035
+
+
+Random Forest Report (Combined Features):
+              precision    recall  f1-score   support
+
+           0       0.98      1.00      0.99       904
+           1       0.99      0.86      0.92       131
+
+    accuracy                           0.98      1035
+   macro avg       0.99      0.93      0.96      1035
+weighted avg       0.98      0.98      0.98      1035
+
+---
+
+## Use Cases
+
+- Spam detection model training  
+- Feature engineering demonstration  
+- Embedding-based similarity and clustering tasks  
+- Educational material for NLP + ML pipelines  
+
+---
+
+## Citation
+
+If you use this dataset, please cite the original SMS Spam Collection dataset:  
+
+> @inproceedings{Almeida2011SpamFiltering, title={Contributions to the Study of SMS Spam Filtering: New Collection and Results}, author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami}, year={2011}, booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)", }.  
+
+---
+
+## License
+
+This dataset is distributed under the same terms as the original SMS Spam dataset (publicly available for research).  
+
+---
+