metadata
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
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}]