lilt-invoices2

Toobese/lilt-invoices2

lilt-invoices2 is a token classification model from Toobese. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: lilt
Access: Public
Created: Aug 22, 2023
Updated: Aug 22, 2023
SHA: 4cc6648c

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Base models

SCUT-DLVCLab/lilt-roberta-en-base

Model card

From the Hugging Face model README

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

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: Aug 22, 2023
Updated: Aug 22, 2023