Update README.md

README.md

@@ -2,12 +2,65 @@
 library_name: transformers
 license: apache-2.0
 base_model: distilbert-base-uncased
+language: en
 tags:
   - text-classification
   - bloom
   - check-in-quality
-language: en
-datasets: CodingInColor-DailySlackCheckins
+  - transformers
+  - fastapi
+datasets:
+  - CodingInColor-DailySlackCheckins
 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}]