Create README.md

README.md

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+---
+license: apache-2.0
+library_name: xgboost
+tags:
+  - text-classification
+  - xgboost
+  - embeddings
+  - sentence-transformers
+  - slack
+  - checkin-detection
+pipeline_tag: text-classification
+model_name: "Check-In or Not Classifier"
+framework: xgboost
+datasets:
+  - custom
+language:
+  - en
+inference:
+  parameters:
+    threshold: 0.5
+---
+
+## 🧠 Check-In vs. Not Check-In Classifier
+
+ This model is a binary text classifier that determines whether a Slack message is a CHECKIN or NOT_CHECKIN. It was trained using a balanced dataset of real Slack check-ins and synthetic non-check-in examples. The model uses sentence-transformer embeddings combined with an XGBoost classifier.
+
+## 🚀 Use Case
+
+This model is designed for automation systems such as Slack bots, n8n workflows, CIC MCP agents, and productivity tracking tools. It detects whether a user message represents real progress or general conversation.
+
+Example predictions:
+
+"Today I refined the workflow automation" → CHECKIN  
+"Does anyone have the Zoom link?" → NOT_CHECKIN
+
+## 📊 Training Details
+
+Dataset size: 4,277 total examples  
+Labels:
+
+- **CHECKIN:** 2,139 real Slack check-ins  
+- **NOT_CHECKIN:** 2,139 synthetic non-check-in samples  
+
+Training achieved **100% accuracy** due to the dataset being clean, consistent, and linearly separable.
+
+## 📁 Repository Structure
+
+```
+checkin_or_not_classifier.json
+README.md
+```
+
+## 🔧 Inference Example
+
+```python
+from sentence_transformers import SentenceTransformer
+import xgboost as xgb
+
+embedder = SentenceTransformer("sentence_transformer_model")
+
+booster = xgb.Booster()
+booster.load_model("checkin_or_not_classifier.json")
+
+def predict(text):
+    emb = embedder.encode([text])
+    pred = booster.predict(xgb.DMatrix(emb))[0]
+    return ("CHECKIN" if pred >= 0.5 else "NOT_CHECKIN", float(pred))
+
+label, score = predict("Today I worked on automation tasks.")
+print(label, score)
+```
+
+## 📘 Intended Use
+
+This model is intended for binary classification of short messages. It is not intended for sentiment analysis, toxicity detection, or general-purpose NLP tasks.
+
+## 📜 License
+
+Apache-2.0
+
+## ✨ Author
+
+Mazamesso “Mazzy” Meba  
+University of North Florida  
+AI Automation & Engineering
+
+
+