Loss: 1.1246
Answer: {'precision': 0.3804878048780488, 'recall': 0.4820766378244747, 'f1': 0.425299890948746, 'number': 809}
Header: {'precision': 0.34408602150537637, 'recall': 0.2689075630252101, 'f1': 0.3018867924528302, 'number': 119}
Question: {'precision': 0.4845360824742268, 'recall': 0.6178403755868545, 'f1': 0.5431283532810565, 'number': 1065}
Overall Precision: 0.4362
Overall Recall: 0.5419
Overall F1: 0.4833
Overall Accuracy: 0.6171

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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.7202	1.0	10	1.4980	{'precision': 0.05310734463276836, 'recall': 0.0580964153275649, 'f1': 0.05548996458087367, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.26246719160104987, 'recall': 0.28169014084507044, 'f1': 0.27173913043478265, 'number': 1065}	0.1711	0.1741	0.1726	0.3625
1.4151	2.0	20	1.3029	{'precision': 0.19834183673469388, 'recall': 0.38442521631644005, 'f1': 0.26167437946992006, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.266388557806913, 'recall': 0.4197183098591549, 'f1': 0.32592052497265767, 'number': 1065}	0.2325	0.3803	0.2886	0.4280
1.259	3.0	30	1.1884	{'precision': 0.2627235213204952, 'recall': 0.4721878862793572, 'f1': 0.3376049491825011, 'number': 809}	{'precision': 0.06349206349206349, 'recall': 0.03361344537815126, 'f1': 0.04395604395604396, 'number': 119}	{'precision': 0.3270588235294118, 'recall': 0.5220657276995305, 'f1': 0.4021699819168174, 'number': 1065}	0.2928	0.4727	0.3616	0.4939
1.1328	4.0	40	1.0951	{'precision': 0.30996309963099633, 'recall': 0.519159456118665, 'f1': 0.3881700554528651, 'number': 809}	{'precision': 0.2857142857142857, 'recall': 0.18487394957983194, 'f1': 0.22448979591836735, 'number': 119}	{'precision': 0.4103139013452915, 'recall': 0.5154929577464789, 'f1': 0.4569288389513109, 'number': 1065}	0.3578	0.4972	0.4161	0.5748
1.0223	5.0	50	1.0810	{'precision': 0.28736581337737405, 'recall': 0.43016069221260816, 'f1': 0.3445544554455445, 'number': 809}	{'precision': 0.37142857142857144, 'recall': 0.2184873949579832, 'f1': 0.2751322751322751, 'number': 119}	{'precision': 0.38396624472573837, 'recall': 0.5981220657276995, 'f1': 0.4676945668135095, 'number': 1065}	0.3439	0.5073	0.4099	0.5856
0.9408	6.0	60	1.0602	{'precision': 0.3160667251975417, 'recall': 0.44499381953028433, 'f1': 0.3696098562628337, 'number': 809}	{'precision': 0.345679012345679, 'recall': 0.23529411764705882, 'f1': 0.27999999999999997, 'number': 119}	{'precision': 0.4154838709677419, 'recall': 0.6046948356807512, 'f1': 0.49254302103250486, 'number': 1065}	0.3726	0.5178	0.4333	0.5983
0.8629	7.0	70	1.0853	{'precision': 0.3160220994475138, 'recall': 0.3535228677379481, 'f1': 0.33372228704784135, 'number': 809}	{'precision': 0.375, 'recall': 0.2773109243697479, 'f1': 0.31884057971014496, 'number': 119}	{'precision': 0.42748091603053434, 'recall': 0.6309859154929578, 'f1': 0.50967007963595, 'number': 1065}	0.3864	0.4972	0.4348	0.5961
0.8089	8.0	80	1.0864	{'precision': 0.35083114610673666, 'recall': 0.4956736711990111, 'f1': 0.4108606557377049, 'number': 809}	{'precision': 0.36904761904761907, 'recall': 0.2605042016806723, 'f1': 0.30541871921182273, 'number': 119}	{'precision': 0.4398051496172582, 'recall': 0.5934272300469483, 'f1': 0.5051958433253397, 'number': 1065}	0.3994	0.5339	0.4569	0.6110
0.7662	9.0	90	1.0967	{'precision': 0.36006974716652135, 'recall': 0.5105067985166872, 'f1': 0.42229038854805717, 'number': 809}	{'precision': 0.4266666666666667, 'recall': 0.2689075630252101, 'f1': 0.32989690721649484, 'number': 119}	{'precision': 0.4724770642201835, 'recall': 0.5802816901408451, 'f1': 0.5208596713021492, 'number': 1065}	0.4202	0.5334	0.4700	0.6115
0.7718	10.0	100	1.1450	{'precision': 0.375, 'recall': 0.5414091470951793, 'f1': 0.44309559939301973, 'number': 809}	{'precision': 0.4050632911392405, 'recall': 0.2689075630252101, 'f1': 0.3232323232323232, 'number': 119}	{'precision': 0.5078125, 'recall': 0.5492957746478874, 'f1': 0.5277401894451962, 'number': 1065}	0.4398	0.5294	0.4804	0.6057
0.6988	11.0	110	1.1180	{'precision': 0.36609829488465395, 'recall': 0.4511742892459827, 'f1': 0.4042081949058693, 'number': 809}	{'precision': 0.3333333333333333, 'recall': 0.2689075630252101, 'f1': 0.29767441860465116, 'number': 119}	{'precision': 0.4661602209944751, 'recall': 0.6338028169014085, 'f1': 0.5372065260644648, 'number': 1065}	0.4219	0.5379	0.4729	0.6089
0.6905	12.0	120	1.1064	{'precision': 0.36837029893924783, 'recall': 0.4721878862793572, 'f1': 0.41386782231852653, 'number': 809}	{'precision': 0.3793103448275862, 'recall': 0.2773109243697479, 'f1': 0.32038834951456313, 'number': 119}	{'precision': 0.47112676056338026, 'recall': 0.6281690140845071, 'f1': 0.5384305835010061, 'number': 1065}	0.4261	0.5439	0.4778	0.6149
0.666	13.0	130	1.1045	{'precision': 0.36981132075471695, 'recall': 0.484548825710754, 'f1': 0.4194756554307116, 'number': 809}	{'precision': 0.3516483516483517, 'recall': 0.2689075630252101, 'f1': 0.3047619047619048, 'number': 119}	{'precision': 0.48205128205128206, 'recall': 0.6178403755868545, 'f1': 0.5415637860082304, 'number': 1065}	0.4300	0.5429	0.4799	0.6174
0.6335	14.0	140	1.1195	{'precision': 0.3810463968410661, 'recall': 0.47713226205191595, 'f1': 0.42371020856201974, 'number': 809}	{'precision': 0.34831460674157305, 'recall': 0.2605042016806723, 'f1': 0.2980769230769231, 'number': 119}	{'precision': 0.4817204301075269, 'recall': 0.6309859154929578, 'f1': 0.5463414634146342, 'number': 1065}	0.4361	0.5464	0.4851	0.6187
0.6277	15.0	150	1.1246	{'precision': 0.3804878048780488, 'recall': 0.4820766378244747, 'f1': 0.425299890948746, 'number': 809}	{'precision': 0.34408602150537637, 'recall': 0.2689075630252101, 'f1': 0.3018867924528302, 'number': 119}	{'precision': 0.4845360824742268, 'recall': 0.6178403755868545, 'f1': 0.5431283532810565, 'number': 1065}	0.4362	0.5419	0.4833	0.6171

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Neha-CanWill/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Neha-CanWill/
layoutlm-funsd