metadata
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: 0.01481
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
- 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
This morning I’ll spend some time organizing scripts and later test a few cases I wrote yesterday.
Output
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} }