lilt-invoices2

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0032
  • Amount: {'precision': 0.9982517482517482, 'recall': 1.0, 'f1': 0.9991251093613298, 'number': 571}
  • Billingaddress: {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161}
  • Description: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 612}
  • Invoicedate: {'precision': 0.9942196531791907, 'recall': 1.0, 'f1': 0.9971014492753623, 'number': 172}
  • Invoicetotal: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 207}
  • Quantity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 545}
  • Subtotal: {'precision': 1.0, 'recall': 0.9933774834437086, 'f1': 0.9966777408637874, 'number': 151}
  • Totaltax: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 139}
  • Unitprice: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 492}
  • Vendorname: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 208}
  • Overall Precision: 0.9994
  • Overall Recall: 0.9994
  • Overall F1: 0.9994
  • Overall Accuracy: 0.9994

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 500

Training results

Training Loss Epoch Step Validation Loss Amount Billingaddress Description Invoicedate Invoicetotal Quantity Subtotal Totaltax Unitprice Vendorname Overall Precision Overall Recall Overall F1 Overall Accuracy
0.6178 4.35 100 0.1659 {'precision': 0.8553654743390358, 'recall': 0.9632224168126094, 'f1': 0.9060955518945634, 'number': 571} {'precision': 0.9815950920245399, 'recall': 0.9937888198757764, 'f1': 0.9876543209876544, 'number': 161} {'precision': 0.9775641025641025, 'recall': 0.9967320261437909, 'f1': 0.9870550161812297, 'number': 612} {'precision': 0.9940476190476191, 'recall': 0.9709302325581395, 'f1': 0.9823529411764705, 'number': 172} {'precision': 0.8571428571428571, 'recall': 0.8985507246376812, 'f1': 0.8773584905660375, 'number': 207} {'precision': 0.9890909090909091, 'recall': 0.998165137614679, 'f1': 0.993607305936073, 'number': 545} {'precision': 0.7664233576642335, 'recall': 0.695364238410596, 'f1': 0.7291666666666665, 'number': 151} {'precision': 0.8818897637795275, 'recall': 0.8057553956834532, 'f1': 0.8421052631578947, 'number': 139} {'precision': 0.9809523809523809, 'recall': 0.8373983739837398, 'f1': 0.9035087719298245, 'number': 492} {'precision': 0.9856459330143541, 'recall': 0.9903846153846154, 'f1': 0.988009592326139, 'number': 208} 0.9368 0.9368 0.9368 0.9368
0.1653 8.7 200 0.0668 {'precision': 0.9420529801324503, 'recall': 0.9964973730297724, 'f1': 0.9685106382978723, 'number': 571} {'precision': 0.9876543209876543, 'recall': 0.9937888198757764, 'f1': 0.9907120743034055, 'number': 161} {'precision': 1.0, 'recall': 0.9901960784313726, 'f1': 0.9950738916256158, 'number': 612} {'precision': 0.9941520467836257, 'recall': 0.9883720930232558, 'f1': 0.9912536443148688, 'number': 172} {'precision': 0.9140271493212669, 'recall': 0.9758454106280193, 'f1': 0.9439252336448598, 'number': 207} {'precision': 0.9945255474452555, 'recall': 1.0, 'f1': 0.9972552607502287, 'number': 545} {'precision': 0.9328358208955224, 'recall': 0.8278145695364238, 'f1': 0.8771929824561403, 'number': 151} {'precision': 0.9615384615384616, 'recall': 0.8992805755395683, 'f1': 0.929368029739777, 'number': 139} {'precision': 0.9978947368421053, 'recall': 0.9634146341463414, 'f1': 0.9803516028955533, 'number': 492} {'precision': 1.0, 'recall': 0.9951923076923077, 'f1': 0.9975903614457832, 'number': 208} 0.9770 0.9770 0.9770 0.9770
0.0676 13.04 300 0.0208 {'precision': 0.9861111111111112, 'recall': 0.9947460595446584, 'f1': 0.990409764603313, 'number': 571} {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 612} {'precision': 0.9941860465116279, 'recall': 0.9941860465116279, 'f1': 0.9941860465116279, 'number': 172} {'precision': 0.9951219512195122, 'recall': 0.9855072463768116, 'f1': 0.9902912621359223, 'number': 207} {'precision': 0.9963369963369964, 'recall': 0.998165137614679, 'f1': 0.9972502291475711, 'number': 545} {'precision': 1.0, 'recall': 0.9602649006622517, 'f1': 0.9797297297297297, 'number': 151} {'precision': 0.9787234042553191, 'recall': 0.9928057553956835, 'f1': 0.9857142857142858, 'number': 139} {'precision': 0.9918864097363083, 'recall': 0.9939024390243902, 'f1': 0.9928934010152284, 'number': 492} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 208} 0.9942 0.9942 0.9942 0.9942
0.0296 17.39 400 0.0067 {'precision': 0.9982456140350877, 'recall': 0.9964973730297724, 'f1': 0.9973707274320772, 'number': 571} {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 612} {'precision': 0.9942196531791907, 'recall': 1.0, 'f1': 0.9971014492753623, 'number': 172} {'precision': 0.9951923076923077, 'recall': 1.0, 'f1': 0.9975903614457832, 'number': 207} {'precision': 0.9981684981684982, 'recall': 1.0, 'f1': 0.999083409715857, 'number': 545} {'precision': 0.9933333333333333, 'recall': 0.9867549668874173, 'f1': 0.9900332225913622, 'number': 151} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 139} {'precision': 0.9979674796747967, 'recall': 0.9979674796747967, 'f1': 0.9979674796747967, 'number': 492} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 208} 0.9982 0.9982 0.9982 0.9982
0.0143 21.74 500 0.0032 {'precision': 0.9982517482517482, 'recall': 1.0, 'f1': 0.9991251093613298, 'number': 571} {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 612} {'precision': 0.9942196531791907, 'recall': 1.0, 'f1': 0.9971014492753623, 'number': 172} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 207} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 545} {'precision': 1.0, 'recall': 0.9933774834437086, 'f1': 0.9966777408637874, 'number': 151} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 139} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 492} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 208} 0.9994 0.9994 0.9994 0.9994

Framework versions

  • Transformers 4.32.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3
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