lilt-en-funsd-2

LinusReply/lilt-en-funsd-2

lilt-en-funsd-2 is a token classification model from LinusReply. 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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Hugging Face

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: lilt
Access: Public
Created: May 24, 2023
Updated: May 24, 2023
SHA: 8130f174

Try a prompt

Datasets

funsd-layoutlmv3

Model card

From the Hugging Face model README

lilt-en-funsd-2

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: 1.7012
Answer: {'precision': 0.901985111662531, 'recall': 0.8898408812729498, 'f1': 0.8958718422674061, 'number': 817}
Header: {'precision': 0.6371681415929203, 'recall': 0.6050420168067226, 'f1': 0.6206896551724138, 'number': 119}
Question: {'precision': 0.8736027515047291, 'recall': 0.9433611884865367, 'f1': 0.9071428571428571, 'number': 1077}
Overall Precision: 0.8718
Overall Recall: 0.9016
Overall F1: 0.8864
Overall Accuracy: 0.8041

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
0.4401	10.53	200	0.9136	{'precision': 0.8668252080856124, 'recall': 0.8922888616891065, 'f1': 0.879372738238842, 'number': 817}	{'precision': 0.512, 'recall': 0.5378151260504201, 'f1': 0.5245901639344263, 'number': 119}	{'precision': 0.8825622775800712, 'recall': 0.9210770659238626, 'f1': 0.9014084507042255, 'number': 1077}	0.8541	0.8867	0.8701	0.8093
0.0458	21.05	400	1.2043	{'precision': 0.879415347137637, 'recall': 0.8837209302325582, 'f1': 0.8815628815628815, 'number': 817}	{'precision': 0.6153846153846154, 'recall': 0.5378151260504201, 'f1': 0.5739910313901345, 'number': 119}	{'precision': 0.8856121537086684, 'recall': 0.9201485608170845, 'f1': 0.9025500910746811, 'number': 1077}	0.8694	0.8828	0.8760	0.8042
0.0127	31.58	600	1.3936	{'precision': 0.880722891566265, 'recall': 0.8947368421052632, 'f1': 0.8876745598057073, 'number': 817}	{'precision': 0.6019417475728155, 'recall': 0.5210084033613446, 'f1': 0.5585585585585585, 'number': 119}	{'precision': 0.8826714801444043, 'recall': 0.9080779944289693, 'f1': 0.8951945080091533, 'number': 1077}	0.8677	0.8798	0.8737	0.8098
0.0082	42.11	800	1.3872	{'precision': 0.8771498771498771, 'recall': 0.8739290085679314, 'f1': 0.8755364806866953, 'number': 817}	{'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119}	{'precision': 0.8669565217391304, 'recall': 0.9257195914577531, 'f1': 0.895374943870678, 'number': 1077}	0.8603	0.8813	0.8707	0.8067
0.0035	52.63	1000	1.6235	{'precision': 0.8825665859564165, 'recall': 0.8922888616891065, 'f1': 0.887401095556908, 'number': 817}	{'precision': 0.49044585987261147, 'recall': 0.6470588235294118, 'f1': 0.5579710144927537, 'number': 119}	{'precision': 0.8833333333333333, 'recall': 0.8857938718662952, 'f1': 0.8845618915159944, 'number': 1077}	0.8531	0.8743	0.8636	0.7953
0.0031	63.16	1200	1.6677	{'precision': 0.9051833122629582, 'recall': 0.8763769889840881, 'f1': 0.890547263681592, 'number': 817}	{'precision': 0.48484848484848486, 'recall': 0.6722689075630253, 'f1': 0.5633802816901409, 'number': 119}	{'precision': 0.8893023255813953, 'recall': 0.8876508820798514, 'f1': 0.8884758364312269, 'number': 1077}	0.8626	0.8703	0.8665	0.7994
0.0014	73.68	1400	1.7012	{'precision': 0.901985111662531, 'recall': 0.8898408812729498, 'f1': 0.8958718422674061, 'number': 817}	{'precision': 0.6371681415929203, 'recall': 0.6050420168067226, 'f1': 0.6206896551724138, 'number': 119}	{'precision': 0.8736027515047291, 'recall': 0.9433611884865367, 'f1': 0.9071428571428571, 'number': 1077}	0.8718	0.9016	0.8864	0.8041
0.0011	84.21	1600	1.6779	{'precision': 0.8715814506539834, 'recall': 0.8971848225214198, 'f1': 0.8841978287092883, 'number': 817}	{'precision': 0.6413043478260869, 'recall': 0.4957983193277311, 'f1': 0.5592417061611374, 'number': 119}	{'precision': 0.8754448398576512, 'recall': 0.9136490250696379, 'f1': 0.894139027714675, 'number': 1077}	0.8634	0.8823	0.8727	0.8067
0.0009	94.74	1800	1.6159	{'precision': 0.8729216152019003, 'recall': 0.8996328029375765, 'f1': 0.8860759493670887, 'number': 817}	{'precision': 0.5740740740740741, 'recall': 0.5210084033613446, 'f1': 0.5462555066079295, 'number': 119}	{'precision': 0.8681898066783831, 'recall': 0.9173630454967502, 'f1': 0.8920993227990971, 'number': 1077}	0.8549	0.8867	0.8705	0.8060
0.0007	105.26	2000	1.5876	{'precision': 0.8733572281959379, 'recall': 0.8947368421052632, 'f1': 0.8839177750906893, 'number': 817}	{'precision': 0.5982142857142857, 'recall': 0.5630252100840336, 'f1': 0.5800865800865801, 'number': 119}	{'precision': 0.8783783783783784, 'recall': 0.9052924791086351, 'f1': 0.8916323731138546, 'number': 1077}	0.8611	0.8808	0.8708	0.8091
0.0003	115.79	2200	1.6529	{'precision': 0.8662721893491124, 'recall': 0.8959608323133414, 'f1': 0.8808664259927798, 'number': 817}	{'precision': 0.5714285714285714, 'recall': 0.5378151260504201, 'f1': 0.554112554112554, 'number': 119}	{'precision': 0.8662587412587412, 'recall': 0.9201485608170845, 'f1': 0.8923908149482216, 'number': 1077}	0.8505	0.8877	0.8687	0.8039
0.0002	126.32	2400	1.6602	{'precision': 0.8699763593380615, 'recall': 0.9008567931456548, 'f1': 0.8851473241130486, 'number': 817}	{'precision': 0.5775862068965517, 'recall': 0.5630252100840336, 'f1': 0.5702127659574467, 'number': 119}	{'precision': 0.8725663716814159, 'recall': 0.9155060352831941, 'f1': 0.8935206162211146, 'number': 1077}	0.8552	0.8887	0.8716	0.8024

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: May 24, 2023
Updated: May 24, 2023