LiLt-funsd-en

jinhybr/LiLt-funsd-en

LiLt-funsd-en is a token classification model from jinhybr. 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 1, 2022
Updated: Dec 1, 2022
SHA: 8a7a3d5d

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Datasets

funsd-layoutlmv3

Spaces

jinhybr/OCR-Document-Parser-LiLt

Model card

From the Hugging Face model README

LiLt-funsd-en

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: 1.7735
Answer: {'precision': 0.8693115519253208, 'recall': 0.9118727050183598, 'f1': 0.8900836320191159, 'number': 817}
Header: {'precision': 0.6630434782608695, 'recall': 0.5126050420168067, 'f1': 0.5781990521327014, 'number': 119}
Question: {'precision': 0.8992673992673993, 'recall': 0.9117920148560817, 'f1': 0.905486399262333, 'number': 1077}
Overall Precision: 0.8760
Overall Recall: 0.8882
Overall F1: 0.8821
Overall Accuracy: 0.8027

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: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.4108	10.53	200	0.9495	{'precision': 0.7949260042283298, 'recall': 0.9204406364749081, 'f1': 0.8530913216108904, 'number': 817}	{'precision': 0.5338345864661654, 'recall': 0.5966386554621849, 'f1': 0.5634920634920635, 'number': 119}	{'precision': 0.8950914340712224, 'recall': 0.8635097493036211, 'f1': 0.8790170132325141, 'number': 1077}	0.8277	0.8708	0.8487	0.7845
0.0402	21.05	400	1.3124	{'precision': 0.8167597765363128, 'recall': 0.8947368421052632, 'f1': 0.8539719626168224, 'number': 817}	{'precision': 0.5071428571428571, 'recall': 0.5966386554621849, 'f1': 0.5482625482625483, 'number': 119}	{'precision': 0.9133398247322297, 'recall': 0.8709377901578459, 'f1': 0.8916349809885932, 'number': 1077}	0.8438	0.8644	0.8540	0.7901
0.0141	31.58	600	1.4747	{'precision': 0.8429378531073446, 'recall': 0.9130966952264382, 'f1': 0.8766157461809635, 'number': 817}	{'precision': 0.6493506493506493, 'recall': 0.42016806722689076, 'f1': 0.5102040816326531, 'number': 119}	{'precision': 0.8861788617886179, 'recall': 0.9108635097493036, 'f1': 0.8983516483516484, 'number': 1077}	0.8589	0.8828	0.8707	0.7950
0.0079	42.11	800	1.6605	{'precision': 0.8132464712269273, 'recall': 0.9167686658506732, 'f1': 0.8619102416570771, 'number': 817}	{'precision': 0.5407407407407407, 'recall': 0.6134453781512605, 'f1': 0.5748031496062992, 'number': 119}	{'precision': 0.8919925512104283, 'recall': 0.8895078922934077, 'f1': 0.8907484890748489, 'number': 1077}	0.8357	0.8843	0.8593	0.7910
0.0064	52.63	1000	1.6328	{'precision': 0.8551564310544612, 'recall': 0.9033047735618115, 'f1': 0.8785714285714284, 'number': 817}	{'precision': 0.7466666666666667, 'recall': 0.47058823529411764, 'f1': 0.5773195876288659, 'number': 119}	{'precision': 0.9029918404351768, 'recall': 0.924791086350975, 'f1': 0.9137614678899083, 'number': 1077}	0.8770	0.8892	0.8831	0.7950
0.0091	63.16	1200	1.6620	{'precision': 0.8280044101433297, 'recall': 0.9192166462668299, 'f1': 0.87122969837587, 'number': 817}	{'precision': 0.6875, 'recall': 0.46218487394957986, 'f1': 0.5527638190954773, 'number': 119}	{'precision': 0.9016697588126159, 'recall': 0.9025069637883009, 'f1': 0.9020881670533641, 'number': 1077}	0.8610	0.8833	0.8720	0.7979
0.0031	73.68	1400	1.7310	{'precision': 0.855039637599094, 'recall': 0.9241126070991432, 'f1': 0.8882352941176471, 'number': 817}	{'precision': 0.7023809523809523, 'recall': 0.4957983193277311, 'f1': 0.58128078817734, 'number': 119}	{'precision': 0.8969917958067457, 'recall': 0.9136490250696379, 'f1': 0.9052437902483901, 'number': 1077}	0.8711	0.8932	0.8820	0.7938
0.002	84.21	1600	1.6978	{'precision': 0.870023419203747, 'recall': 0.9094247246022031, 'f1': 0.8892878515858766, 'number': 817}	{'precision': 0.6987951807228916, 'recall': 0.48739495798319327, 'f1': 0.5742574257425742, 'number': 119}	{'precision': 0.9021237303785781, 'recall': 0.9071494893221913, 'f1': 0.9046296296296297, 'number': 1077}	0.8802	0.8833	0.8817	0.8048
0.0023	94.74	1800	1.5996	{'precision': 0.8639534883720931, 'recall': 0.9094247246022031, 'f1': 0.8861061419200955, 'number': 817}	{'precision': 0.6629213483146067, 'recall': 0.4957983193277311, 'f1': 0.5673076923076922, 'number': 119}	{'precision': 0.8969641214351426, 'recall': 0.9052924791086351, 'f1': 0.9011090573012939, 'number': 1077}	0.8728	0.8828	0.8777	0.8026
0.0009	105.26	2000	1.7239	{'precision': 0.8851674641148325, 'recall': 0.9057527539779682, 'f1': 0.8953418027828192, 'number': 817}	{'precision': 0.5726495726495726, 'recall': 0.5630252100840336, 'f1': 0.5677966101694915, 'number': 119}	{'precision': 0.889487870619946, 'recall': 0.9192200557103064, 'f1': 0.9041095890410958, 'number': 1077}	0.8698	0.8927	0.8811	0.8008
0.0009	115.79	2200	1.6091	{'precision': 0.8576349024110218, 'recall': 0.9143206854345165, 'f1': 0.8850710900473934, 'number': 817}	{'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119}	{'precision': 0.8901098901098901, 'recall': 0.9025069637883009, 'f1': 0.896265560165975, 'number': 1077}	0.8630	0.8857	0.8742	0.8093
0.0003	126.32	2400	1.7210	{'precision': 0.8627450980392157, 'recall': 0.9155446756425949, 'f1': 0.8883610451306414, 'number': 817}	{'precision': 0.6555555555555556, 'recall': 0.4957983193277311, 'f1': 0.5645933014354068, 'number': 119}	{'precision': 0.8873994638069705, 'recall': 0.9220055710306406, 'f1': 0.9043715846994536, 'number': 1077}	0.8671	0.8942	0.8804	0.8072
0.0006	136.84	2600	1.8215	{'precision': 0.8293478260869566, 'recall': 0.9339045287637698, 'f1': 0.8785261945883708, 'number': 817}	{'precision': 0.5887850467289719, 'recall': 0.5294117647058824, 'f1': 0.5575221238938053, 'number': 119}	{'precision': 0.9232227488151659, 'recall': 0.904363974001857, 'f1': 0.9136960600375235, 'number': 1077}	0.8646	0.8942	0.8791	0.7946
0.0002	147.37	2800	1.8084	{'precision': 0.875886524822695, 'recall': 0.9069767441860465, 'f1': 0.8911605532170775, 'number': 817}	{'precision': 0.5752212389380531, 'recall': 0.5462184873949579, 'f1': 0.5603448275862069, 'number': 119}	{'precision': 0.8896860986547085, 'recall': 0.9210770659238626, 'f1': 0.9051094890510949, 'number': 1077}	0.8669	0.8932	0.8799	0.8000
0.0002	157.89	3000	1.8224	{'precision': 0.9067164179104478, 'recall': 0.8922888616891065, 'f1': 0.8994447871684145, 'number': 817}	{'precision': 0.6222222222222222, 'recall': 0.47058823529411764, 'f1': 0.5358851674641149, 'number': 119}	{'precision': 0.8937112488928255, 'recall': 0.9368616527390901, 'f1': 0.9147778785131461, 'number': 1077}	0.8868	0.8912	0.8890	0.8082
0.0005	168.42	3200	1.7383	{'precision': 0.8754406580493537, 'recall': 0.9118727050183598, 'f1': 0.8932853717026379, 'number': 817}	{'precision': 0.6451612903225806, 'recall': 0.5042016806722689, 'f1': 0.5660377358490566, 'number': 119}	{'precision': 0.8995475113122172, 'recall': 0.9229340761374187, 'f1': 0.9110907424381303, 'number': 1077}	0.8780	0.8937	0.8858	0.8117
0.0002	178.95	3400	1.7757	{'precision': 0.885954381752701, 'recall': 0.9033047735618115, 'f1': 0.8945454545454545, 'number': 817}	{'precision': 0.6896551724137931, 'recall': 0.5042016806722689, 'f1': 0.5825242718446602, 'number': 119}	{'precision': 0.9043321299638989, 'recall': 0.9303621169916435, 'f1': 0.917162471395881, 'number': 1077}	0.8876	0.8942	0.8909	0.8104
0.0001	189.47	3600	1.7467	{'precision': 0.8645833333333334, 'recall': 0.9143206854345165, 'f1': 0.8887566924449734, 'number': 817}	{'precision': 0.6354166666666666, 'recall': 0.5126050420168067, 'f1': 0.5674418604651162, 'number': 119}	{'precision': 0.9027522935779817, 'recall': 0.9136490250696379, 'f1': 0.9081679741578219, 'number': 1077}	0.8741	0.8902	0.8821	0.8054
0.0002	200.0	3800	1.7730	{'precision': 0.8631090487238979, 'recall': 0.9106487148102815, 'f1': 0.8862418106015486, 'number': 817}	{'precision': 0.6354166666666666, 'recall': 0.5126050420168067, 'f1': 0.5674418604651162, 'number': 119}	{'precision': 0.8952205882352942, 'recall': 0.904363974001857, 'f1': 0.899769053117783, 'number': 1077}	0.8695	0.8838	0.8766	0.8024
0.0001	210.53	4000	1.7735	{'precision': 0.8693115519253208, 'recall': 0.9118727050183598, 'f1': 0.8900836320191159, 'number': 817}	{'precision': 0.6630434782608695, 'recall': 0.5126050420168067, 'f1': 0.5781990521327014, 'number': 119}	{'precision': 0.8992673992673993, 'recall': 0.9117920148560817, 'f1': 0.905486399262333, 'number': 1077}	0.8760	0.8882	0.8821	0.8027

Framework versions

Transformers 4.24.0
Pytorch 1.12.1
Datasets 2.7.1
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: Dec 1, 2022
Updated: Dec 1, 2022