Add English model card

README.md

@@ -0,0 +1,92 @@
+---
+license: apache-2.0
+language:
+- en
+tags:
+- OneScience
+- Earth Science
+- Land Surface
+- CLM5
+- Model Emulation
+- Parameter Estimation
+frameworks: PyTorch
+---
+
+<p align="center"><strong><span style="font-size: 30px;">CLM5-Emulator</span></strong></p>
+
+# Model Introduction
+
+CLM5-Emulator uses machine learning to emulate global biophysical responses of Community Land Model version 5. Six parameters drive predictions of GPP and LHF EOF components and bounded parameter estimation.
+
+Paper: A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5  
+https://doi.org/10.5194/ascmo-6-223-2020
+
+# Model Description
+
+The model was proposed by researchers at NCAR and CERFACS. It was trained with GSWP3-driven CLM5 parameter perturbation ensembles and FLUXNET-MTE observational targets. Two independent feed-forward networks emulate GPP and LHF spatial components for surrogate modeling and biophysical parameter estimation.
+
+# Use Cases
+
+| Use Case | Description |
+| :---: | :--- |
+| CLM5 emulation | Predict GPP and LHF components from six parameters. |
+| Parameter estimation | Search the bounded normalized parameter space. |
+| Spatial reconstruction | Reconstruct global responses from EOF components. |
+| ModelScope/OneCode execution | Validate structured data, training, inference, metrics, and visualization. |
+| Multi-GPU training | Start multi-process training through `torchrun`. |
+
+# Usage Instructions
+
+```bash
+hf download OneScience-Group/CLM5-Emulator --local-dir ./CLM5-Emulator
+cd CLM5-Emulator
+```
+
+### Environment Dependencies
+
+**Hardware Requirements**
+
+- A GPU or DCU is recommended.
+- A CPU can be used for connectivity validation with the default small-sample configuration.
+- DCU users should install DTK 25.04.2 or a compatible OneScience-recommended version first.
+
+**DCU Environment**
+
+```bash
+# Activate DTK and Conda first
+conda create -n onescience311 python=3.11 -y
+conda activate onescience311
+pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
+```
+
+**GPU Environment**
+
+```bash
+# Activate Conda first
+conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
+conda activate onescience311
+pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
+```
+
+```bash
+python scripts/fake_data.py
+python scripts/train.py
+torchrun --nproc_per_node=2 --nnodes=1 --master_addr="localhost" --master_port=29500 scripts/train.py
+python scripts/inference.py
+python scripts/result.py
+```
+
+Synthetic data retain `[B,6]` inputs, independent GPP/LHF targets, three EOF modes, and the logical 4-by-5-degree grid. Both target networks participate in backpropagation, and single-process and two-process DDP training have been verified. Inference restores the checkpoint, produces components with shape `[8,2,3]` and fields with shape `[8,2,46,72]`, and verifies finite outputs. Training results are saved to `result/checkpoints/clm5_emulator.pt`; inference and evaluation results are saved under `result/output/` and `result/evaluation/`.
+
+# Official OneScience Information
+
+| Platform | OneScience Main Repository | Skills Repository |
+|---|---|---|
+| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
+| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
+
+# Citation and License
+
+This repository is an independent engineering reproduction of the public CLM5-Emulator specifications.
+
+The original paper is licensed under CC BY 4.0; the paper, CLM5, GSWP3, and FLUXNET-MTE data remain subject to their respective licenses and terms.