Update README.md

README.md

@@ -1,3 +1,35 @@
----
-license: mit
----
+---
+library_name: sklearn
+tags:
+- tabular-regression
+- machine-learning
+- civil-engineering
+datasets:
+- custom
+license: mit
+---
+
+# Model Name: c3cf
+
+## Model Description
+
+**c3cf**, Cascade Forest models for predicting the Compressive strength of Coal-ash-incorporated Cement composites, were developed in the research article: [Coal ashes as supplementary cementitious materials: physicochemical property effects on hydration and strength, along with property-informed machine learning modeling](). They are tree-based ensemble models that implements the [deep forest](https://github.com/LAMDA-NJU/Deep-Forest) algorithm. 
+
+- **Developed by:** Kangyi Cai @ Missouri S&T
+- **Model type:** Cascade Forest
+- **Language(s):** Python
+- **License:** MIT
+
+## Uses & Limitations
+**c3cf** can make reasonable predictions for coal-ash-incorporated cement mortars, whose strength is in the range of 15-65 MPa, replacement level of coal ash is <50% by mass, and curing age is between 7 and 91 days.
+
+## How to Get Started with the Model
+This repository contains a collection of regression models located in the `regs/` directory. Refer to the [kycai/c3cf](https://github.com/kycai/c3cf) for detailed guides.
+
+```python
+from huggingface_hub import snapshot_download
+path = snapshot_download(repo_id="kycai23/c3cf")
+```
+
+## Training & Evaluation
+Refer to the [research article]() mentioned above.