README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - funsd-layoutlmv3
model-index:
  - name: lilt-en-funsd
    results: []

lilt-en-funsd

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:

• Loss: 1.5496
• Answer: {'precision': 0.875, 'recall': 0.9253365973072215, 'f1': 0.8994646044021416, 'number': 817}
• Header: {'precision': 0.6276595744680851, 'recall': 0.4957983193277311, 'f1': 0.5539906103286385, 'number': 119}
• Question: {'precision': 0.9049360146252285, 'recall': 0.9192200557103064, 'f1': 0.9120221096269001, 'number': 1077}
• Overall Precision: 0.8796
• Overall Recall: 0.8967
• Overall F1: 0.8881
• Overall Accuracy: 0.8134

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: 2500

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.434	10.53	200	1.0227	{'precision': 0.8357705286839145, 'recall': 0.9094247246022031, 'f1': 0.8710433763188746, 'number': 817}	{'precision': 0.7058823529411765, 'recall': 0.40336134453781514, 'f1': 0.5133689839572192, 'number': 119}	{'precision': 0.8683522231909329, 'recall': 0.924791086350975, 'f1': 0.89568345323741, 'number': 1077}	0.8493	0.8877	0.8681	0.7935
0.0484	21.05	400	1.3626	{'precision': 0.8098360655737705, 'recall': 0.9069767441860465, 'f1': 0.8556581986143187, 'number': 817}	{'precision': 0.6086956521739131, 'recall': 0.47058823529411764, 'f1': 0.5308056872037914, 'number': 119}	{'precision': 0.8613333333333333, 'recall': 0.8997214484679665, 'f1': 0.8801089918256131, 'number': 1077}	0.8283	0.8773	0.8521	0.7995
0.0168	31.58	600	1.3003	{'precision': 0.8440046565774156, 'recall': 0.8873929008567931, 'f1': 0.8651551312649164, 'number': 817}	{'precision': 0.6421052631578947, 'recall': 0.5126050420168067, 'f1': 0.5700934579439252, 'number': 119}	{'precision': 0.8776595744680851, 'recall': 0.9192200557103064, 'f1': 0.8979591836734694, 'number': 1077}	0.8530	0.8823	0.8674	0.8189
0.008	42.11	800	1.3225	{'precision': 0.8584795321637427, 'recall': 0.8984088127294981, 'f1': 0.8779904306220095, 'number': 817}	{'precision': 0.5736434108527132, 'recall': 0.6218487394957983, 'f1': 0.596774193548387, 'number': 119}	{'precision': 0.888468809073724, 'recall': 0.872794800371402, 'f1': 0.8805620608899298, 'number': 1077}	0.8560	0.8684	0.8621	0.8210
0.0059	52.63	1000	1.6362	{'precision': 0.8307522123893806, 'recall': 0.9192166462668299, 'f1': 0.8727484020918072, 'number': 817}	{'precision': 0.6419753086419753, 'recall': 0.4369747899159664, 'f1': 0.52, 'number': 119}	{'precision': 0.8944444444444445, 'recall': 0.8969359331476323, 'f1': 0.8956884561891516, 'number': 1077}	0.8567	0.8788	0.8676	0.8061
0.0027	63.16	1200	1.6927	{'precision': 0.8269858541893362, 'recall': 0.9302325581395349, 'f1': 0.8755760368663594, 'number': 817}	{'precision': 0.6046511627906976, 'recall': 0.4369747899159664, 'f1': 0.5073170731707317, 'number': 119}	{'precision': 0.9000925069380203, 'recall': 0.903435468895079, 'f1': 0.901760889712697, 'number': 1077}	0.8557	0.8867	0.8709	0.7939
0.002	73.68	1400	1.4609	{'precision': 0.8479467258601554, 'recall': 0.9351285189718482, 'f1': 0.889406286379511, 'number': 817}	{'precision': 0.5726495726495726, 'recall': 0.5630252100840336, 'f1': 0.5677966101694915, 'number': 119}	{'precision': 0.8917431192660551, 'recall': 0.9025069637883009, 'f1': 0.8970927549607752, 'number': 1077}	0.8553	0.8957	0.8750	0.7965
0.0012	84.21	1600	1.4851	{'precision': 0.865909090909091, 'recall': 0.9326805385556916, 'f1': 0.8980553918680023, 'number': 817}	{'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119}	{'precision': 0.9008341056533827, 'recall': 0.9025069637883009, 'f1': 0.901669758812616, 'number': 1077}	0.8708	0.8937	0.8821	0.8131
0.0006	94.74	1800	1.5228	{'precision': 0.850613154960981, 'recall': 0.9339045287637698, 'f1': 0.8903150525087514, 'number': 817}	{'precision': 0.594059405940594, 'recall': 0.5042016806722689, 'f1': 0.5454545454545453, 'number': 119}	{'precision': 0.896709323583181, 'recall': 0.9108635097493036, 'f1': 0.9037309995393827, 'number': 1077}	0.8623	0.8962	0.8789	0.8082
0.0004	105.26	2000	1.5287	{'precision': 0.867579908675799, 'recall': 0.9302325581395349, 'f1': 0.8978145304193739, 'number': 817}	{'precision': 0.6222222222222222, 'recall': 0.47058823529411764, 'f1': 0.5358851674641149, 'number': 119}	{'precision': 0.8917710196779964, 'recall': 0.9257195914577531, 'f1': 0.9084282460136676, 'number': 1077}	0.8700	0.9006	0.8850	0.8128
0.0003	115.79	2200	1.5306	{'precision': 0.8766006984866124, 'recall': 0.9216646266829865, 'f1': 0.8985680190930787, 'number': 817}	{'precision': 0.6263736263736264, 'recall': 0.4789915966386555, 'f1': 0.5428571428571428, 'number': 119}	{'precision': 0.8902765388046388, 'recall': 0.9266480965645311, 'f1': 0.908098271155596, 'number': 1077}	0.8730	0.8982	0.8854	0.8127
0.0001	126.32	2400	1.5496	{'precision': 0.875, 'recall': 0.9253365973072215, 'f1': 0.8994646044021416, 'number': 817}	{'precision': 0.6276595744680851, 'recall': 0.4957983193277311, 'f1': 0.5539906103286385, 'number': 119}	{'precision': 0.9049360146252285, 'recall': 0.9192200557103064, 'f1': 0.9120221096269001, 'number': 1077}	0.8796	0.8967	0.8881	0.8134

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu118
• Datasets 2.13.0
• Tokenizers 0.13.3