alusci
/

distilbert-smsafe

@@ -1,199 +1,135 @@
 ---
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 ---
 library_name: transformers
+tags:
+  - text-classification
+  - spam-detection
+  - sms
+license: apache-2.0
 ---
+# 🛡️ Model Card for `alusci/distilbert-smsafe`
+A lightweight DistilBERT model fine-tuned for spam detection in SMS messages. The model classifies input messages as either **spam** or **ham** (not spam), using a custom dataset of real-world OTP (One-Time Password) and spam SMS messages.
+---
 ## Model Details
 ### Model Description
+- **Developed by:** [alusci](https://huggingface.co/alusci)
+- **Model type:** Transformer-based binary classifier
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** `distilbert-base-uncased`
+### Model Sources
+- **Repository:** [https://huggingface.co/alusci/distilbert-smsafe](https://huggingface.co/alusci/distilbert-smsafe)
+---
+## 🛠️ Uses
 ### Direct Use
+- Detect whether an SMS message is spam or ham (OTP or not).
+- Useful in prototypes, educational settings, or lightweight filtering applications.
+```python
+from transformers import pipeline
+classifier = pipeline("text-classification", model="alusci/distilbert-smsafe")
+result = classifier("Your verification code is 123456. Please do not share it with anyone.")
+# Optional: map the label to human-readable terms
+label_map = {"LABEL_0": "ham", "LABEL_1": "spam"}
+print(f"Label: {label_map[result[0]['label']]} - Score: {result[0]['score']:.2f}")
+```
 ### Out-of-Scope Use
+- Not intended for email spam detection or multilingual message filtering.
+- Not suitable for production environments without further testing and evaluation.
+---
+## 🧪 Bias, Risks, and Limitations
+- The model may reflect dataset biases (e.g., message structure, language patterns).
+- It may misclassify legitimate OTPs or non-standard spam content.
+- Risk of false positives in edge cases.
 ### Recommendations
+- Evaluate on your own SMS dataset before deployment.
+- Consider combining with rule-based or heuristic systems in production.
+---
+## 📚 Training Details
 ### Training Data
+- Dataset used: [`alusci/sms-otp-spam-dataset`](https://huggingface.co/datasets/alusci/sms-otp-spam-dataset)
+- Binary labels for spam and non-spam OTP messages
 ### Training Procedure
+- **Epochs:** 5
+- **Batch Size:** 16 (assumed)
+- **Loss Function:** CrossEntropyLoss
+- **Optimizer:** AdamW
+- **Tokenizer:** `distilbert-base-uncased`
+---
+## 📈 Evaluation
+### Metrics
+- Accuracy, Precision, Recall, F1-score on held-out validation set
+- Binary classification labels:
+  - `LABEL_0` → ham
+  - `LABEL_1` → spam
 ### Results
+**Evaluation metrics after 5 epochs:**
+- **Loss:** 0.2962
+- **Accuracy:** 91.35%
+- **Precision:** 90.26%
+- **Recall:** 100.00%
+- **F1-score:** 94.88%
+**Performance:**
+- **Evaluation runtime:** 4.37 seconds
+- **Samples/sec:** 457.27
+- **Steps/sec:** 9.15
+---
+## 🌱 Environmental Impact
+- **Hardware Type:** Apple Silicon MPS GPU (Mac)
+- **Hours used:** <1 hour (small dataset)
+- **Cloud Provider:** None (trained locally)
+- **Carbon Emitted:** Minimal due to local and efficient hardware
+---
+## 🔧 Technical Specifications
 ### Model Architecture and Objective
+- **Base:** DistilBERT
+- **Objective:** Binary classification head on pooled output
+- **Parameters:** ~66M (same as distilbert)
+---
+## 📬 Model Card Contact
+For questions or feedback, please contact via [Hugging Face profile](https://huggingface.co/alusci).