Tabular Classification
Transformers
Safetensors
felatab
feature-extraction
fela
tabular
in-context-learning
prior-fitted-network
foundation-model
delta-rule
cpu
on-device
custom_code
Eval Results (legacy)
Instructions to use lowdown-labs/fela-tab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lowdown-labs/fela-tab with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lowdown-labs/fela-tab", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| dataset,model,device,roc_auc,log_loss,accuracy,f1_macro,fit_s,latency_ms,peak_ram_mb,peak_vram_mb,status,notes | |
| credit-g,FelaTab-big,gpu,0.7635,0.525,0.735,0.6619,0.0,4.551,233.0,5297.0,ok, | |
| credit-g,FelaTab-small,gpu,0.7685,0.5162,0.715,0.6273,0.0,1.247,18.0,5271.0,ok, | |
| credit-g,TabFM,gpu,0.783,0.4996,0.735,0.6696,0.27,23.476,126.0,5465.0,ok, | |
| credit-g,XGBoost,cpu,0.7431,0.6554,0.725,0.6644,5.1,0.065,8.0,,ok, | |
| credit-g,LightGBM,cpu,0.7268,1.0375,0.7,0.6429,2.38,0.009,4.0,,ok, | |
| blood-transfusion,FelaTab-big,gpu,0.7773,0.4584,0.7867,0.6032,0.0,2.139,1.0,5287.0,ok, | |
| blood-transfusion,FelaTab-small,gpu,0.6701,0.5843,0.7067,0.5298,0.0,1.123,1.0,5268.0,ok, | |
| blood-transfusion,TabFM,gpu,0.7945,0.4463,0.7933,0.6643,0.06,17.228,2.0,5331.0,ok, | |
| blood-transfusion,XGBoost,cpu,0.7617,0.5444,0.74,0.633,1.23,0.021,1.0,,ok, | |
| blood-transfusion,LightGBM,cpu,0.7393,0.6274,0.7533,0.6445,6.55,0.017,3.0,,ok, | |
| churn,FelaTab-big,gpu,0.8606,0.2831,0.896,0.7279,0.0,1.412,5.0,5445.0,ok, | |
| churn,FelaTab-small,gpu,0.8637,0.2807,0.897,0.7401,0.0,0.84,5.0,5347.0,ok, | |
| churn,TabFM,gpu,0.9408,0.1019,0.975,0.9453,0.25,29.61,58.0,6145.0,ok, | |
| churn,XGBoost,cpu,0.9285,0.1674,0.958,0.9084,2.58,0.003,1.0,,ok, | |
| churn,LightGBM,cpu,0.9283,0.2441,0.959,0.9079,0.94,0.002,4.0,,ok, | |
| electricity,FelaTab-big,gpu,0.8102,0.6046,0.7087,0.7014,0.0,0.461,17.0,5847.0,ok, | |
| electricity,FelaTab-small,gpu,0.8099,0.5942,0.713,0.7007,0.0,0.274,17.0,5548.0,ok, | |
| electricity,TabFM,gpu,0.995,0.0906,0.9629,0.9621,0.23,96.876,231.0,9723.0,ok, | |
| electricity,XGBoost,cpu,0.9692,0.2327,0.904,0.9017,1.62,0.002,12.0,,ok, | |
| electricity,LightGBM,cpu,0.9717,0.2243,0.9104,0.9083,2.57,0.001,6.0,,ok, | |
| vehicle,FelaTab-big,gpu,0.9431,0.4473,0.7765,0.776,0.0,2.63,8.0,5294.0,ok, | |
| vehicle,FelaTab-small,gpu,0.9422,0.4559,0.7882,0.7872,0.0,1.206,1.0,5270.0,ok, | |
| vehicle,TabFM,gpu,0.982,0.2259,0.9,0.8978,0.22,22.292,9.0,5416.0,ok, | |
| vehicle,XGBoost,cpu,0.934,0.4907,0.7824,0.7826,11.57,0.078,1.0,,ok, | |
| vehicle,LightGBM,cpu,0.9363,0.8884,0.7765,0.7745,8.64,0.024,8.0,,ok, | |
| segment,FelaTab-big,gpu,0.9947,0.2035,0.9242,0.9231,0.0,1.824,3.0,5362.0,ok, | |
| segment,FelaTab-small,gpu,0.9964,0.1783,0.9372,0.9372,0.0,1.092,3.0,5305.0,ok, | |
| segment,TabFM,gpu,0.9998,0.0365,0.987,0.9871,0.24,23.559,25.0,5712.0,ok, | |
| segment,XGBoost,cpu,0.9996,0.0612,0.9784,0.9785,3.85,0.023,4.0,,ok, | |
| segment,LightGBM,cpu,0.9995,0.0862,0.9805,0.9805,8.34,0.007,8.0,,ok, | |
| jungle_chess,FelaTab-big,gpu,0.9106,0.5325,0.7773,0.6066,0.0,0.46,16.0,5839.0,ok, | |
| jungle_chess,FelaTab-small,gpu,0.905,0.5482,0.7685,0.6096,0.0,0.274,16.0,5545.0,ok, | |
| jungle_chess,TabFM,gpu,0.9838,0.1912,0.8881,0.84,0.15,94.106,182.0,9121.0,ok, | |
| jungle_chess,XGBoost,cpu,0.9754,0.2564,0.8618,0.8117,19.06,0.003,2.0,,ok, | |
| jungle_chess,LightGBM,cpu,0.9772,0.2483,0.8666,0.8149,12.73,0.004,12.0,,ok, | |
| adult,FelaTab-big,gpu,0.8795,0.3861,0.8303,0.7445,0.0,0.567,234.0,5880.0,ok, | |
| adult,FelaTab-small,gpu,0.8757,0.3824,0.8172,0.7139,0.0,0.273,21.0,5566.0,ok, | |
| adult,TabFM,gpu,0.9317,0.2712,0.8781,0.8207,0.52,112.3,407.0,10226.0,ok, | |
| adult,XGBoost,cpu,0.9305,0.2721,0.8772,0.8204,5.77,0.002,12.0,,ok, | |
| adult,LightGBM,cpu,0.9293,0.2743,0.8746,0.8171,11.14,0.001,9.0,,ok, | |