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trng1305/
layoutlm-sroie-dacn_v1

Name: layoutlm-sroie-dacn_v1
Availability: InStock

generated_from_trainer

endpoints_compatible

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layoutlm-sroie-dacn_v1

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1261
Address: {'precision': 0.9915730337078652, 'recall': 0.993857179421551, 'f1': 0.9927137926626612, 'number': 3907}
Company: {'precision': 0.9749670619235836, 'recall': 0.9926224010731053, 'f1': 0.9837155201063477, 'number': 1491}
Date: {'precision': 0.9976470588235294, 'recall': 0.9906542056074766, 'f1': 0.9941383352872216, 'number': 428}
Total: {'precision': 0.88, 'recall': 0.889487870619946, 'f1': 0.8847184986595173, 'number': 371}
Overall Precision: 0.9812
Overall Recall: 0.9871
Overall F1: 0.9842
Overall Accuracy: 0.9952

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: 3e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20
label_smoothing_factor: 0.02

Training results

Training Loss	Epoch	Step	Validation Loss	Address	Company	Date	Total	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.3889	1.0	40	0.1566	{'precision': 0.980352644836272, 'recall': 0.9961607371384694, 'f1': 0.9881934746730989, 'number': 3907}	{'precision': 0.8888888888888888, 'recall': 0.9872568745808182, 'f1': 0.9354941213854464, 'number': 1491}	{'precision': 0.821917808219178, 'recall': 0.9813084112149533, 'f1': 0.8945686900958466, 'number': 428}	{'precision': 0.7297297297297297, 'recall': 0.2183288409703504, 'f1': 0.3360995850622407, 'number': 371}	0.9387	0.9464	0.9425	0.9826
0.1424	2.0	80	0.1317	{'precision': 0.9943430187708923, 'recall': 0.9897619657025851, 'f1': 0.9920472036942023, 'number': 3907}	{'precision': 0.9666448659254414, 'recall': 0.9912810194500336, 'f1': 0.9788079470198675, 'number': 1491}	{'precision': 0.9766899766899767, 'recall': 0.9789719626168224, 'f1': 0.9778296382730455, 'number': 428}	{'precision': 0.65, 'recall': 0.8409703504043127, 'f1': 0.7332549941245595, 'number': 371}	0.9603	0.9805	0.9703	0.9909
0.127	3.0	120	0.1261	{'precision': 0.9915665729619217, 'recall': 0.9930893268492449, 'f1': 0.9923273657289002, 'number': 3907}	{'precision': 0.9710906701708278, 'recall': 0.9912810194500336, 'f1': 0.981081978094922, 'number': 1491}	{'precision': 0.9905660377358491, 'recall': 0.9813084112149533, 'f1': 0.9859154929577464, 'number': 428}	{'precision': 0.8130081300813008, 'recall': 0.8086253369272237, 'f1': 0.8108108108108109, 'number': 371}	0.9759	0.9808	0.9784	0.9935
0.1204	4.0	160	0.1278	{'precision': 0.9865550481988838, 'recall': 0.9953928845661633, 'f1': 0.9909542616893872, 'number': 3907}	{'precision': 0.9737015121630507, 'recall': 0.9932930918846412, 'f1': 0.9833997343957503, 'number': 1491}	{'precision': 0.997624703087886, 'recall': 0.9813084112149533, 'f1': 0.9893992932862191, 'number': 428}	{'precision': 0.7989556135770235, 'recall': 0.8247978436657682, 'f1': 0.8116710875331564, 'number': 371}	0.9727	0.9837	0.9782	0.9934
0.1176	5.0	200	0.1246	{'precision': 0.9915708812260536, 'recall': 0.9936012285641157, 'f1': 0.9925850166197903, 'number': 3907}	{'precision': 0.9691803278688524, 'recall': 0.9912810194500336, 'f1': 0.980106100795756, 'number': 1491}	{'precision': 0.9952718676122931, 'recall': 0.9836448598130841, 'f1': 0.9894242068155112, 'number': 428}	{'precision': 0.8050632911392405, 'recall': 0.8571428571428571, 'f1': 0.8302872062663185, 'number': 371}	0.9746	0.9842	0.9794	0.9939
0.1151	6.0	240	0.1246	{'precision': 0.9915816326530612, 'recall': 0.9948809828512926, 'f1': 0.9932285677782037, 'number': 3907}	{'precision': 0.9736668861092824, 'recall': 0.9919517102615694, 'f1': 0.9827242524916943, 'number': 1491}	{'precision': 0.997624703087886, 'recall': 0.9813084112149533, 'f1': 0.9893992932862191, 'number': 428}	{'precision': 0.8381201044386423, 'recall': 0.8652291105121294, 'f1': 0.8514588859416445, 'number': 371}	0.9782	0.9855	0.9818	0.9945
0.1133	7.0	280	0.1246	{'precision': 0.9918179493735617, 'recall': 0.9928333759918095, 'f1': 0.9923254029163469, 'number': 3907}	{'precision': 0.9762532981530343, 'recall': 0.9926224010731053, 'f1': 0.9843698037911539, 'number': 1491}	{'precision': 1.0, 'recall': 0.985981308411215, 'f1': 0.9929411764705882, 'number': 428}	{'precision': 0.8514588859416445, 'recall': 0.8652291105121294, 'f1': 0.8582887700534758, 'number': 371}	0.9801	0.9847	0.9824	0.9946
0.1126	8.0	320	0.1244	{'precision': 0.9915773353751914, 'recall': 0.9943690811364219, 'f1': 0.9929712460063898, 'number': 3907}	{'precision': 0.9736842105263158, 'recall': 0.9926224010731053, 'f1': 0.9830621056127532, 'number': 1491}	{'precision': 1.0, 'recall': 0.9906542056074766, 'f1': 0.9953051643192489, 'number': 428}	{'precision': 0.8423772609819121, 'recall': 0.8787061994609164, 'f1': 0.8601583113456465, 'number': 371}	0.9786	0.9868	0.9826	0.9947
0.1106	9.0	360	0.1245	{'precision': 0.9905708460754332, 'recall': 0.9948809828512926, 'f1': 0.9927212361128847, 'number': 3907}	{'precision': 0.9832999331997327, 'recall': 0.9872568745808182, 'f1': 0.9852744310575635, 'number': 1491}	{'precision': 1.0, 'recall': 0.9906542056074766, 'f1': 0.9953051643192489, 'number': 428}	{'precision': 0.9106628242074928, 'recall': 0.8517520215633423, 'f1': 0.8802228412256267, 'number': 371}	0.9850	0.9842	0.9846	0.9953
0.1104	10.0	400	0.1252	{'precision': 0.9918242207460398, 'recall': 0.9936012285641157, 'f1': 0.9927119294207902, 'number': 3907}	{'precision': 0.9736147757255936, 'recall': 0.9899396378269618, 'f1': 0.9817093448619887, 'number': 1491}	{'precision': 0.9976470588235294, 'recall': 0.9906542056074766, 'f1': 0.9941383352872216, 'number': 428}	{'precision': 0.8885793871866295, 'recall': 0.8598382749326146, 'f1': 0.873972602739726, 'number': 371}	0.9818	0.9845	0.9832	0.9949
0.1094	11.0	440	0.1257	{'precision': 0.9918263090676884, 'recall': 0.993857179421551, 'f1': 0.9928407057018664, 'number': 3907}	{'precision': 0.9754316069057105, 'recall': 0.9852448021462106, 'f1': 0.9803136469803136, 'number': 1491}	{'precision': 1.0, 'recall': 0.9906542056074766, 'f1': 0.9953051643192489, 'number': 428}	{'precision': 0.8849315068493151, 'recall': 0.8706199460916442, 'f1': 0.8777173913043478, 'number': 371}	0.9821	0.9842	0.9832	0.9949
0.11	12.0	480	0.1261	{'precision': 0.9908233494774408, 'recall': 0.9948809828512926, 'f1': 0.9928480204342274, 'number': 3907}	{'precision': 0.9742744063324539, 'recall': 0.9906103286384976, 'f1': 0.98237445959428, 'number': 1491}	{'precision': 1.0, 'recall': 0.9906542056074766, 'f1': 0.9953051643192489, 'number': 428}	{'precision': 0.8659517426273459, 'recall': 0.8706199460916442, 'f1': 0.8682795698924732, 'number': 371}	0.9800	0.9861	0.9830	0.9948
0.1088	13.0	520	0.1256	{'precision': 0.9915730337078652, 'recall': 0.993857179421551, 'f1': 0.9927137926626612, 'number': 3907}	{'precision': 0.97556142668428, 'recall': 0.9906103286384976, 'f1': 0.9830282861896837, 'number': 1491}	{'precision': 1.0, 'recall': 0.9906542056074766, 'f1': 0.9953051643192489, 'number': 428}	{'precision': 0.8703703703703703, 'recall': 0.8867924528301887, 'f1': 0.8785046728971964, 'number': 371}	0.9809	0.9864	0.9837	0.9950
0.1084	14.0	560	0.1255	{'precision': 0.9910668708524758, 'recall': 0.993857179421551, 'f1': 0.9924600638977635, 'number': 3907}	{'precision': 0.9755775577557756, 'recall': 0.9912810194500336, 'f1': 0.9833666001330672, 'number': 1491}	{'precision': 1.0, 'recall': 0.9906542056074766, 'f1': 0.9953051643192489, 'number': 428}	{'precision': 0.8661417322834646, 'recall': 0.889487870619946, 'f1': 0.8776595744680851, 'number': 371}	0.9803	0.9868	0.9835	0.9950
0.1079	15.0	600	0.1252	{'precision': 0.9908116385911179, 'recall': 0.9936012285641157, 'f1': 0.9922044728434504, 'number': 3907}	{'precision': 0.9762062128222075, 'recall': 0.9906103286384976, 'f1': 0.9833555259653795, 'number': 1491}	{'precision': 1.0, 'recall': 0.9883177570093458, 'f1': 0.9941245593419507, 'number': 428}	{'precision': 0.9030470914127424, 'recall': 0.8787061994609164, 'f1': 0.8907103825136613, 'number': 371}	0.9828	0.9856	0.9842	0.9952
0.1077	16.0	640	0.1258	{'precision': 0.9900484817555499, 'recall': 0.9930893268492449, 'f1': 0.9915665729619217, 'number': 3907}	{'precision': 0.9755775577557756, 'recall': 0.9912810194500336, 'f1': 0.9833666001330672, 'number': 1491}	{'precision': 0.9976470588235294, 'recall': 0.9906542056074766, 'f1': 0.9941383352872216, 'number': 428}	{'precision': 0.8885869565217391, 'recall': 0.8814016172506739, 'f1': 0.884979702300406, 'number': 371}	0.9811	0.9858	0.9834	0.9950
0.1077	17.0	680	0.1265	{'precision': 0.9908069458631257, 'recall': 0.9930893268492449, 'f1': 0.9919468234692573, 'number': 3907}	{'precision': 0.97556142668428, 'recall': 0.9906103286384976, 'f1': 0.9830282861896837, 'number': 1491}	{'precision': 0.9976470588235294, 'recall': 0.9906542056074766, 'f1': 0.9941383352872216, 'number': 428}	{'precision': 0.8796791443850267, 'recall': 0.8867924528301887, 'f1': 0.8832214765100671, 'number': 371}	0.9809	0.9860	0.9834	0.9950
0.1075	18.0	720	0.1262	{'precision': 0.9915687276443536, 'recall': 0.9933452777066804, 'f1': 0.9924562076460811, 'number': 3907}	{'precision': 0.9749835418038183, 'recall': 0.9932930918846412, 'f1': 0.9840531561461794, 'number': 1491}	{'precision': 0.9976470588235294, 'recall': 0.9906542056074766, 'f1': 0.9941383352872216, 'number': 428}	{'precision': 0.8858695652173914, 'recall': 0.8787061994609164, 'f1': 0.8822733423545331, 'number': 371}	0.9817	0.9863	0.9840	0.9951
0.1075	19.0	760	0.1262	{'precision': 0.9915730337078652, 'recall': 0.993857179421551, 'f1': 0.9927137926626612, 'number': 3907}	{'precision': 0.9749670619235836, 'recall': 0.9926224010731053, 'f1': 0.9837155201063477, 'number': 1491}	{'precision': 0.9976470588235294, 'recall': 0.9906542056074766, 'f1': 0.9941383352872216, 'number': 428}	{'precision': 0.8823529411764706, 'recall': 0.889487870619946, 'f1': 0.8859060402684563, 'number': 371}	0.9814	0.9871	0.9842	0.9952
0.1074	20.0	800	0.1261	{'precision': 0.9915730337078652, 'recall': 0.993857179421551, 'f1': 0.9927137926626612, 'number': 3907}	{'precision': 0.9749670619235836, 'recall': 0.9926224010731053, 'f1': 0.9837155201063477, 'number': 1491}	{'precision': 0.9976470588235294, 'recall': 0.9906542056074766, 'f1': 0.9941383352872216, 'number': 428}	{'precision': 0.88, 'recall': 0.889487870619946, 'f1': 0.8847184986595173, 'number': 371}	0.9812	0.9871	0.9842	0.9952

Framework versions

Transformers 4.28.0
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.13.3

trng1305/layoutlm-sroie-dacn_v1

layoutlm-sroie-dacn_v1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

trng1305/
layoutlm-sroie-dacn_v1