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
File size: 1,910 Bytes
98024ab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | #!/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 |