Download config.json from OneScience-Group/ACE2-Seasonal: direct link, hf CLI and curl.
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https://huggingface.co/OneScience-Group/ACE2-Seasonal/resolve/main/config.json
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hf download hf://OneScience-Group/ACE2-Seasonal/config.json
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curl -L -o config.json https://huggingface.co/OneScience-Group/ACE2-Seasonal/resolve/main/config.json
881 Bytes
| {"model_name":"ACE2-Seasonal","model_type":"ace2_seasonal","architectures":["ACE2Seasonal"],"framework":"PyTorch","domain":"seasonal-weather","task":"global-seasonal-ensemble-prediction","implementation":{"entry_point":"model/ace2_seasonal.py","scope":"L2 seasonal hindcast protocol reproduction"},"architecture":{"logical_grid":[180,360],"engineering_channels":8,"channel_ledger_status":"engineering because the seasonal paper omits the complete ACE2 channel list","time_step_hours":6,"ensemble_members":64,"boundary_method":"persistent SST and sea-ice anomalies over seasonal climatology"},"data":{"training":"ERA5","hindcast_years":[1993,2015],"independent_years":[2001,2010],"target_season":"DJF","synthetic_tiles":true},"configuration_sources":["conf/config.yaml","model/ace2_seasonal.py","scripts/fake_data.py","scripts/train.py","scripts/inference.py","scripts/result.py"]} | |