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
| { | |
| "tokenizer_class": "YieldTokenizer", | |
| "auto_map": { | |
| "AutoTokenizer": [ | |
| "tokenization_yield.YieldTokenizer", | |
| null | |
| ] | |
| }, | |
| "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" | |
| ], | |
| "K": 52, | |
| "eval_cutoffs": [ | |
| 20, | |
| 24, | |
| 28, | |
| 32, | |
| 36, | |
| 40, | |
| 44, | |
| 48, | |
| 52 | |
| ], | |
| "model_max_length": 1000000 | |
| } |