lilt-en-funsd

tkazusa/lilt-en-funsd

lilt-en-funsd is a token classification model from tkazusa. 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: Dec 7, 2022
Updated: Dec 15, 2022
SHA: 9ab159b3

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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: 1.6459
Answer: {'precision': 0.8831942789034565, 'recall': 0.9069767441860465, 'f1': 0.894927536231884, 'number': 817}
Header: {'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119}
Question: {'precision': 0.8998178506375227, 'recall': 0.9173630454967502, 'f1': 0.9085057471264367, 'number': 1077}
Overall Precision: 0.8789
Overall Recall: 0.8907
Overall F1: 0.8848
Overall Accuracy: 0.8068

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: 2000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.4201	10.53	200	0.8003	{'precision': 0.8321995464852607, 'recall': 0.8984088127294981, 'f1': 0.8640376692171865, 'number': 817}	{'precision': 0.5714285714285714, 'recall': 0.5714285714285714, 'f1': 0.5714285714285714, 'number': 119}	{'precision': 0.8651079136690647, 'recall': 0.89322191272052, 'f1': 0.8789401553220649, 'number': 1077}	0.8348	0.8763	0.8551	0.8104
0.0376	21.05	400	1.3158	{'precision': 0.8395904436860068, 'recall': 0.9033047735618115, 'f1': 0.8702830188679245, 'number': 817}	{'precision': 0.4785714285714286, 'recall': 0.5630252100840336, 'f1': 0.5173745173745175, 'number': 119}	{'precision': 0.8887814313346228, 'recall': 0.8532961931290622, 'f1': 0.8706774040738986, 'number': 1077}	0.8397	0.8564	0.8480	0.7934
0.0119	31.58	600	1.4791	{'precision': 0.8752941176470588, 'recall': 0.9106487148102815, 'f1': 0.8926214757048591, 'number': 817}	{'precision': 0.5401459854014599, 'recall': 0.6218487394957983, 'f1': 0.578125, 'number': 119}	{'precision': 0.8818681318681318, 'recall': 0.8941504178272981, 'f1': 0.8879668049792531, 'number': 1077}	0.8567	0.8847	0.8705	0.7961
0.0061	42.11	800	1.5605	{'precision': 0.8617886178861789, 'recall': 0.9082007343941249, 'f1': 0.8843861740166865, 'number': 817}	{'precision': 0.5963302752293578, 'recall': 0.5462184873949579, 'f1': 0.5701754385964912, 'number': 119}	{'precision': 0.8747763864042933, 'recall': 0.9080779944289693, 'f1': 0.8911161731207289, 'number': 1077}	0.8549	0.8867	0.8705	0.7965
0.0026	52.63	1000	1.5172	{'precision': 0.8596491228070176, 'recall': 0.8996328029375765, 'f1': 0.8791866028708135, 'number': 817}	{'precision': 0.7176470588235294, 'recall': 0.5126050420168067, 'f1': 0.5980392156862744, 'number': 119}	{'precision': 0.8737864077669902, 'recall': 0.9192200557103064, 'f1': 0.8959276018099548, 'number': 1077}	0.8616	0.8872	0.8742	0.8014
0.0019	63.16	1200	1.6132	{'precision': 0.8735224586288416, 'recall': 0.9045287637698899, 'f1': 0.888755261575466, 'number': 817}	{'precision': 0.6460176991150443, 'recall': 0.6134453781512605, 'f1': 0.6293103448275863, 'number': 119}	{'precision': 0.881508078994614, 'recall': 0.9117920148560817, 'f1': 0.8963943404837974, 'number': 1077}	0.8654	0.8912	0.8781	0.8040
0.0012	73.68	1400	1.6459	{'precision': 0.8831942789034565, 'recall': 0.9069767441860465, 'f1': 0.894927536231884, 'number': 817}	{'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119}	{'precision': 0.8998178506375227, 'recall': 0.9173630454967502, 'f1': 0.9085057471264367, 'number': 1077}	0.8789	0.8907	0.8848	0.8068
0.0005	84.21	1600	1.5619	{'precision': 0.8602771362586605, 'recall': 0.9118727050183598, 'f1': 0.8853238265002972, 'number': 817}	{'precision': 0.6631578947368421, 'recall': 0.5294117647058824, 'f1': 0.5887850467289719, 'number': 119}	{'precision': 0.8944494995450409, 'recall': 0.9127205199628597, 'f1': 0.9034926470588234, 'number': 1077}	0.8694	0.8897	0.8795	0.8155
0.0003	94.74	1800	1.6571	{'precision': 0.8649592549476135, 'recall': 0.9094247246022031, 'f1': 0.886634844868735, 'number': 817}	{'precision': 0.6391752577319587, 'recall': 0.5210084033613446, 'f1': 0.5740740740740741, 'number': 119}	{'precision': 0.8971792538671519, 'recall': 0.9155060352831941, 'f1': 0.90625, 'number': 1077}	0.8715	0.8897	0.8805	0.8098
0.0003	105.26	2000	1.6731	{'precision': 0.8672875436554133, 'recall': 0.9118727050183598, 'f1': 0.8890214797136038, 'number': 817}	{'precision': 0.62, 'recall': 0.5210084033613446, 'f1': 0.5662100456621004, 'number': 119}	{'precision': 0.9008264462809917, 'recall': 0.9108635097493036, 'f1': 0.9058171745152355, 'number': 1077}	0.8730	0.8882	0.8806	0.8071

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu117
Datasets 2.7.1
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: Dec 7, 2022
Updated: Dec 15, 2022