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library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
language: en
tags:
- text-classification
- bloom
- check-in-quality
- transformers
- fastapi
datasets:
- user6295018/checkin-quality-dataset
metrics:
- accuracy
- f1
- precision
- recall
pipeline_tag: text-classification
model-index:
- name: Bloom Check-in Quality Classifier
results: []
---
# 🌸 Bloom Check-in Quality Classifier
The **Bloom Check-in Quality Classifier** is a fine-tuned `DistilBERT` model designed to analyze daily check-ins from the *Coding in Color* program and classify them into one of three categories:
- **Descriptive** — Clear, thoughtful, and specific check-ins
- **Neutral** — Somewhat informative but missing depth
- **Vague** — Minimal or unclear updates
This model powers Bloom AI’s productivity assistant, which helps students reflect on their daily work habits and communicate effectively.
---
## 🧠 Model Details
- **Base model:** `distilbert-base-uncased`
- **Framework:** 🤗 Transformers + PyTorch
- **Language:** English
- **Task:** Text Classification
- **Labels:** `["vague", "neutral", "descriptive"]`
---
## 📊 Training Information
- **Dataset:** 1,200+ anonymized check-ins from the Coding in Color program
- **Split:** 80% train / 10% validation / 10% test
- **Epochs:** 3
- **Batch size:** 16
- **Optimizer:** AdamW
- **Learning rate:** 5e-5
---
## ⚙️ Inference Example
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="user6295018/checkin-quality-classifier")
classifier("Had a really productive day working on my API and debugging the UI.")
# [{'label': 'descriptive', 'score': 0.94}] |