---
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
import numpy as np
from huggingface_hub import hf_hub_download

# ----------------------------------------------------
# 1. Load the embedding model (must be downloaded locally)
# ----------------------------------------------------
# Users must install: pip install sentence-transformers
embedder = SentenceTransformer("all-MiniLM-L6-v2")

# ----------------------------------------------------
# 2. Download your XGBoost model from HuggingFace Hub
# ----------------------------------------------------
model_path = hf_hub_download(
    repo_id="mjpsm/checkin_or_not_model",
    filename="checkin_or_not_classifier.json"
)

# Load the model
booster = xgb.Booster()
booster.load_model(model_path)

# ----------------------------------------------------
# 3. Prediction function
# ----------------------------------------------------
def predict(text: str):
    emb = embedder.encode([text])
    dmatrix = xgb.DMatrix(emb)

    score = float(booster.predict(dmatrix)[0])
    label = "CHECKIN" if score >= 0.5 else "NOT_CHECKIN"

    return {"label": label, "score": score}


# ----------------------------------------------------
# 4. Example usage
# ----------------------------------------------------
example = "Today I worked on improving the automation workflow."
result = predict(example)

print(result)

```

## 📘 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