lilt-en-funsd

kravilal/lilt-en-funsd

lilt-en-funsd is a token classification model from kravilal. 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: Apr 27, 2023
Updated: Apr 27, 2023
SHA: 741647b2

Try a prompt

Datasets

funsd-layoutlmv3

Model card

From the Hugging Face model README

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: 1.4588
Answer: {'precision': 0.8786057692307693, 'recall': 0.8947368421052632, 'f1': 0.88659793814433, 'number': 817}
Header: {'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119}
Question: {'precision': 0.8854351687388987, 'recall': 0.9257195914577531, 'f1': 0.9051293690422152, 'number': 1077}
Overall Precision: 0.8705
Overall Recall: 0.8917
Overall F1: 0.8810
Overall Accuracy: 0.8222

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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.4177	10.53	200	0.9741	{'precision': 0.834106728538283, 'recall': 0.8800489596083231, 'f1': 0.8564621798689696, 'number': 817}	{'precision': 0.6363636363636364, 'recall': 0.4117647058823529, 'f1': 0.5, 'number': 119}	{'precision': 0.8831985624438454, 'recall': 0.9127205199628597, 'f1': 0.897716894977169, 'number': 1077}	0.8533	0.8698	0.8615	0.8139
0.0528	21.05	400	1.2793	{'precision': 0.8391845979614949, 'recall': 0.9069767441860465, 'f1': 0.871764705882353, 'number': 817}	{'precision': 0.5480769230769231, 'recall': 0.4789915966386555, 'f1': 0.5112107623318385, 'number': 119}	{'precision': 0.878645343367827, 'recall': 0.8672237697307336, 'f1': 0.8728971962616822, 'number': 1077}	0.8449	0.8604	0.8526	0.8057
0.0154	31.58	600	1.3635	{'precision': 0.8705463182897862, 'recall': 0.8971848225214198, 'f1': 0.8836648583484027, 'number': 817}	{'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119}	{'precision': 0.8776041666666666, 'recall': 0.9387186629526463, 'f1': 0.9071332436069988, 'number': 1077}	0.8638	0.8977	0.8804	0.8164
0.0082	42.11	800	1.4185	{'precision': 0.8700361010830325, 'recall': 0.8849449204406364, 'f1': 0.8774271844660194, 'number': 817}	{'precision': 0.6428571428571429, 'recall': 0.6050420168067226, 'f1': 0.6233766233766234, 'number': 119}	{'precision': 0.8921124206708976, 'recall': 0.9136490250696379, 'f1': 0.9027522935779816, 'number': 1077}	0.8695	0.8838	0.8766	0.8212
0.0038	52.63	1000	1.4588	{'precision': 0.8786057692307693, 'recall': 0.8947368421052632, 'f1': 0.88659793814433, 'number': 817}	{'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119}	{'precision': 0.8854351687388987, 'recall': 0.9257195914577531, 'f1': 0.9051293690422152, 'number': 1077}	0.8705	0.8917	0.8810	0.8222
0.0026	63.16	1200	1.5730	{'precision': 0.8666666666666667, 'recall': 0.8910648714810282, 'f1': 0.8786964393482196, 'number': 817}	{'precision': 0.7073170731707317, 'recall': 0.48739495798319327, 'f1': 0.5771144278606964, 'number': 119}	{'precision': 0.8887884267631103, 'recall': 0.9127205199628597, 'f1': 0.9005955107650022, 'number': 1077}	0.8723	0.8788	0.8755	0.8139
0.0015	73.68	1400	1.6294	{'precision': 0.837471783295711, 'recall': 0.9082007343941249, 'f1': 0.8714034057545508, 'number': 817}	{'precision': 0.6530612244897959, 'recall': 0.5378151260504201, 'f1': 0.5898617511520737, 'number': 119}	{'precision': 0.9039179104477612, 'recall': 0.8997214484679665, 'f1': 0.9018147975802697, 'number': 1077}	0.8633	0.8818	0.8725	0.8173
0.001	84.21	1600	1.6406	{'precision': 0.8434684684684685, 'recall': 0.9167686658506732, 'f1': 0.8785923753665689, 'number': 817}	{'precision': 0.6260869565217392, 'recall': 0.6050420168067226, 'f1': 0.6153846153846154, 'number': 119}	{'precision': 0.9001865671641791, 'recall': 0.8960074280408542, 'f1': 0.8980921358771522, 'number': 1077}	0.8607	0.8872	0.8738	0.8140
0.0006	94.74	1800	1.6743	{'precision': 0.8525714285714285, 'recall': 0.9130966952264382, 'f1': 0.8817966903073285, 'number': 817}	{'precision': 0.6666666666666666, 'recall': 0.5042016806722689, 'f1': 0.5741626794258373, 'number': 119}	{'precision': 0.8982584784601283, 'recall': 0.9099350046425255, 'f1': 0.904059040590406, 'number': 1077}	0.8687	0.8872	0.8779	0.8082
0.0003	105.26	2000	1.7003	{'precision': 0.8696682464454977, 'recall': 0.8984088127294981, 'f1': 0.8838049367850691, 'number': 817}	{'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119}	{'precision': 0.8927272727272727, 'recall': 0.9117920148560817, 'f1': 0.9021589343132751, 'number': 1077}	0.8721	0.8808	0.8764	0.8110
0.0002	115.79	2200	1.7767	{'precision': 0.8564867967853043, 'recall': 0.9130966952264382, 'f1': 0.8838862559241707, 'number': 817}	{'precision': 0.64, 'recall': 0.5378151260504201, 'f1': 0.5844748858447488, 'number': 119}	{'precision': 0.9077212806026366, 'recall': 0.8950789229340761, 'f1': 0.9013557737260401, 'number': 1077}	0.8726	0.8813	0.8769	0.8004
0.0002	126.32	2400	1.7093	{'precision': 0.8546910755148741, 'recall': 0.9143206854345165, 'f1': 0.8835008870490834, 'number': 817}	{'precision': 0.6413043478260869, 'recall': 0.4957983193277311, 'f1': 0.5592417061611374, 'number': 119}	{'precision': 0.8956602031394275, 'recall': 0.9006499535747446, 'f1': 0.8981481481481481, 'number': 1077}	0.8668	0.8823	0.8744	0.8027

Framework versions

Transformers 4.28.1
Pytorch 1.13.1+cu117
Datasets 2.11.0
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: Apr 27, 2023
Updated: Apr 27, 2023