README.md

---
language: en
pipeline_tag: summarization
tags:
- text2text-generation
- internship
- check-in
- daily-tasks
- bart
license: mit
datasets:
- custom
metrics:
- rouge
model-index:
- name: Check-In Expansion Model (BART)
  results:
  - task:
      name: Text2Text Generation
      type: text2text-generation
    dataset:
      name: Custom Internship Check-in Dataset
      type: json
    metrics:
    - name: ROUGE-1
      type: rouge
      value: 0.014810
---

# Check-In Expansion Model (BART fine-tuned)

This is a fine-tuned version of **facebook/bart-base** for **internship/tech daily check-in expansions**.  
It takes a short input (a concise task description) and outputs a more polished, detailed check-in note.  

## 🧩 Model Details
- **Base Model**: [facebook/bart-base](https://huggingface.co/facebook/bart-base)  
- **Fine-tuned On**: 1130 examples (910 train / 220 eval) of custom internship-style check-in data.  
- **Task**: Expand a short input task into a more structured daily check-in.  
- **Language**: English  

## 🛠️ Intended Uses
- Writing structured **internship progress updates**.  
- Generating more detailed **check-in notes** from short inputs.  
- Automating team updates in workflows (e.g., via **n8n + Hugging Face API**).  

## 🚀 Example

**Input**
```text
This morning I’ll spend some time organizing scripts and later test a few cases I wrote yesterday.
```
**Output**
```text
Today, I’ll begin by organizing the scripts in my repository to make the project structure more manageable. Afterward, I’ll rerun the test cases I wrote yesterday to confirm they pass consistently. My objective is to reduce clutter, improve reliability, and ensure the codebase is stable before I move forward.
```
📊 Evaluation

Trained with ROUGE evaluation metrics.

ROUGE-1: ~ 0.014810

⚖️ License

MIT License

📎 Citation

If you use this model, please cite:

@misc{checkin_bart_2025,
  title={Check-In Expansion Model (BART fine-tuned)},
  author={Anderson, Dequan},
  year={2025},
  publisher={Hugging Face}
}