Tabular Regression
ONNX
LiteRT
Keras
PyTorch
LiteRT
industrial
pump
digital-twin
edge-ai
onnxruntime
tensorflow
anomaly-detection
Instructions to use sankalpsthakur/forge-pump-surrogate-multiruntime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use sankalpsthakur/forge-pump-surrogate-multiruntime with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| step,train_loss,validation_loss | |
| 1.0,0.9963008761405945,0.9899876117706299 | |
| 50.0,0.20535214245319366,0.19592496752738953 | |
| 100.0,0.07981108874082565,0.08101140707731247 | |
| 150.0,0.055988844484090805,0.05515463650226593 | |
| 200.0,0.04116128757596016,0.03957992047071457 | |
| 250.0,0.02848229371011257,0.029451480135321617 | |
| 300.0,0.021913500502705574,0.022344013676047325 | |
| 350.0,0.01797110214829445,0.017676711082458496 | |
| 400.0,0.01641218177974224,0.014862108044326305 | |
| 450.0,0.011328869499266148,0.012844718061387539 | |
| 500.0,0.011695842258632183,0.011314745992422104 | |
| 550.0,0.01150362566113472,0.010173065587878227 | |
| 600.0,0.008094939403235912,0.00920979492366314 | |
| 650.0,0.008898338302969933,0.008438476361334324 | |
| 700.0,0.0071515305899083614,0.0077940961346030235 | |
| 750.0,0.0073469155468046665,0.007158683612942696 | |
| 800.0,0.006369061768054962,0.006723754107952118 | |
| 850.0,0.006099874619394541,0.006282842718064785 | |
| 900.0,0.005429540295153856,0.005760681815445423 | |
| 950.0,0.005471603479236364,0.005379386246204376 | |
| 1000.0,0.0045560565777122974,0.0049425107426941395 | |
| 1050.0,0.004560902714729309,0.00464222114533186 | |
| 1100.0,0.0038316771388053894,0.004249736201018095 | |
| 1150.0,0.004041003063321114,0.003938440233469009 | |
| 1200.0,0.0033754874020814896,0.0036329030990600586 | |
| 1250.0,0.0031946918461471796,0.0034491943661123514 | |
| 1300.0,0.002978973090648651,0.003168969415128231 | |
| 1350.0,0.0023275779094547033,0.003058691741898656 | |
| 1400.0,0.0029261186718940735,0.002790183061733842 | |
| 1450.0,0.002608848735690117,0.0026526344008743763 | |
| 1500.0,0.00278308242559433,0.002589526353403926 | |
| 1550.0,0.002822611480951309,0.0023886635899543762 | |
| 1600.0,0.001974353101104498,0.0022914824075996876 | |
| 1650.0,0.0019695537630468607,0.0022022968623787165 | |
| 1700.0,0.0021250101272016764,0.0021320374216884375 | |
| 1750.0,0.0019593548495322466,0.002090318826958537 | |
| 1800.0,0.0020189606584608555,0.001985583920031786 | |
| 1850.0,0.0013471875572577119,0.0018599381437525153 | |
| 1900.0,0.0018203849904239178,0.0017766630044206977 | |
| 1950.0,0.0012311465106904507,0.0016940782079473138 | |
| 2000.0,0.001966660376638174,0.0016044304938986897 | |
| 2050.0,0.0017897057114169002,0.0016407514922320843 | |
| 2100.0,0.0013370063388720155,0.0015471476363018155 | |
| 2150.0,0.001394768594764173,0.0014726885128766298 | |
| 2200.0,0.0014963404973968863,0.0014483900740742683 | |
| 2250.0,0.0016188900917768478,0.0014655422419309616 | |
| 2300.0,0.0011775546008720994,0.001378241227939725 | |
| 2350.0,0.00152510404586792,0.0014376547187566757 | |
| 2400.0,0.0010531543521210551,0.001309236860834062 | |
| 2450.0,0.0016270267078652978,0.0013164394767954946 | |
| 2500.0,0.001329936902038753,0.0012587867677211761 | |