Text Classification
Transformers
Safetensors
yield-weather-soil
crop-yield
multi-temporal
regression
yield-estimation
custom_code
Instructions to use ICICLE-AI/yield-estimation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ICICLE-AI/yield-estimation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ICICLE-AI/yield-estimation", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("ICICLE-AI/yield-estimation", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| #!/bin/bash | |
| #SBATCH --job-name=yield-cornbelt | |
| #SBATCH --mail-type=ALL | |
| #SBATCH --mail-user=sridhar.86@buckeyemail.osu.edu | |
| #SBATCH --time=08:00:00 | |
| #SBATCH --account=PAS2699 | |
| #SBATCH --output=yield-training.%j.out | |
| #SBATCH --mem=100G | |
| #SBATCH --gpus-per-node=1 | |
| module load python/3.12 | |
| source activate yield_hf | |
| python scripts/train_hf.py \ | |
| --train_file data/cornbelt/train.h5 \ | |
| --val_file data/cornbelt/val.h5 \ | |
| --test_file data/cornbelt/test.h5 \ | |
| --weather_vars prcp,srad,swe,tmax,tmin,vp \ | |
| --soil_vars bdod_mean_0-5cm,bdod_mean_5-15cm,bdod_mean_15-30cm,bdod_mean_30-60cm,bdod_mean_60-100cm,bdod_mean_100-200cm,cec_mean_0-5cm,cec_mean_5-15cm,cec_mean_15-30cm,cec_mean_30-60cm,cec_mean_60-100cm,cec_mean_100-200cm,cfvo_mean_0-5cm,cfvo_mean_5-15cm,cfvo_mean_15-30cm,cfvo_mean_30-60cm,cfvo_mean_60-100cm,cfvo_mean_100-200cm,clay_mean_0-5cm,clay_mean_5-15cm,clay_mean_15-30cm,clay_mean_30-60cm,clay_mean_60-100cm,clay_mean_100-200cm,nitrogen_mean_0-5cm,nitrogen_mean_5-15cm,nitrogen_mean_15-30cm,nitrogen_mean_30-60cm,nitrogen_mean_60-100cm,nitrogen_mean_100-200cm,ocd_mean_0-5cm,ocd_mean_5-15cm,ocd_mean_15-30cm,ocd_mean_30-60cm,ocd_mean_60-100cm,ocd_mean_100-200cm,ocs_mean_0-5cm,ocs_mean_5-15cm,ocs_mean_15-30cm,ocs_mean_30-60cm,ocs_mean_60-100cm,ocs_mean_100-200cm,phh2o_mean_0-5cm,phh2o_mean_5-15cm,phh2o_mean_15-30cm,phh2o_mean_30-60cm,phh2o_mean_60-100cm,phh2o_mean_100-200cm,sand_mean_0-5cm,sand_mean_5-15cm,sand_mean_15-30cm,sand_mean_30-60cm,sand_mean_60-100cm,sand_mean_100-200cm,silt_mean_0-5cm,silt_mean_5-15cm,silt_mean_15-30cm,silt_mean_30-60cm,silt_mean_60-100cm,silt_mean_100-200cm,soc_mean_0-5cm,soc_mean_5-15cm,soc_mean_15-30cm,soc_mean_30-60cm,soc_mean_60-100cm,soc_mean_100-200cm \ | |
| --crop corn \ | |
| --time_agg weekly \ | |
| --train_cutoffs 20,24,28,32,36,40,44,48,52 \ | |
| --eval_cutoffs 20,24,28,32,36,40,44,48,52 \ | |
| --epochs 30 \ | |
| --lr 3e-5 \ | |
| --batch_size 32 \ | |
| --out_dir checkpoints |