ananth-docai1

ananthrgv/ananth-docai1

ananth-docai1 is a token classification model from ananthrgv. Use it when you need labels on individual words, such as names. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
Model type: layoutlm
Access: Public
Created: Dec 16, 2022
Updated: Dec 16, 2022
SHA: b223690d

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Datasets

funsd

Model card

From the Hugging Face model README

ananth-docai1

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the funsd dataset. It achieves the following results on the evaluation set:

Loss: 0.7024
Answer: {'precision': 0.7113513513513513, 'recall': 0.8133498145859085, 'f1': 0.7589388696655133, 'number': 809}
Header: {'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}
Question: {'precision': 0.7811387900355872, 'recall': 0.8244131455399061, 'f1': 0.8021927820922796, 'number': 1065}
Overall Precision: 0.7241
Overall Recall: 0.7903
Overall F1: 0.7558
Overall Accuracy: 0.8106

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: 3e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.7944	1.0	10	1.6233	{'precision': 0.01929260450160772, 'recall': 0.014833127317676144, 'f1': 0.016771488469601678, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.27685325264750377, 'recall': 0.17183098591549295, 'f1': 0.2120509849362688, 'number': 1065}	0.1520	0.0978	0.1190	0.3505
1.5001	2.0	20	1.2971	{'precision': 0.11125, 'recall': 0.1100123609394314, 'f1': 0.11062771908017402, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4044059795436664, 'recall': 0.48262910798122066, 'f1': 0.4400684931506849, 'number': 1065}	0.2912	0.3026	0.2968	0.5348
1.136	3.0	30	0.9852	{'precision': 0.4911699779249448, 'recall': 0.5500618046971569, 'f1': 0.5189504373177842, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.6086587436332768, 'recall': 0.6732394366197183, 'f1': 0.6393223361569326, 'number': 1065}	0.5562	0.5830	0.5693	0.6941
0.8567	4.0	40	0.8143	{'precision': 0.627744510978044, 'recall': 0.7775030902348579, 'f1': 0.6946438431805633, 'number': 809}	{'precision': 0.06666666666666667, 'recall': 0.01680672268907563, 'f1': 0.026845637583892617, 'number': 119}	{'precision': 0.6987179487179487, 'recall': 0.7164319248826291, 'f1': 0.7074640704682429, 'number': 1065}	0.6563	0.6994	0.6772	0.7467
0.6998	5.0	50	0.7133	{'precision': 0.6534859521331946, 'recall': 0.7762669962917181, 'f1': 0.7096045197740113, 'number': 809}	{'precision': 0.2, 'recall': 0.11764705882352941, 'f1': 0.14814814814814817, 'number': 119}	{'precision': 0.7243532560214094, 'recall': 0.7624413145539906, 'f1': 0.7429094236047575, 'number': 1065}	0.6757	0.7296	0.7016	0.7781
0.5886	6.0	60	0.6775	{'precision': 0.648406374501992, 'recall': 0.8046971569839307, 'f1': 0.7181467181467182, 'number': 809}	{'precision': 0.25806451612903225, 'recall': 0.13445378151260504, 'f1': 0.17679558011049723, 'number': 119}	{'precision': 0.712947189097104, 'recall': 0.7859154929577464, 'f1': 0.7476552032157214, 'number': 1065}	0.6714	0.7546	0.7106	0.7890
0.5185	7.0	70	0.6770	{'precision': 0.6755888650963597, 'recall': 0.7799752781211372, 'f1': 0.7240390131956398, 'number': 809}	{'precision': 0.2079207920792079, 'recall': 0.17647058823529413, 'f1': 0.19090909090909092, 'number': 119}	{'precision': 0.7341337907375644, 'recall': 0.8037558685446009, 'f1': 0.7673688928731511, 'number': 1065}	0.6851	0.7566	0.7191	0.7955
0.4672	8.0	80	0.6729	{'precision': 0.683083511777302, 'recall': 0.788627935723115, 'f1': 0.7320711417096959, 'number': 809}	{'precision': 0.23300970873786409, 'recall': 0.20168067226890757, 'f1': 0.21621621621621623, 'number': 119}	{'precision': 0.747431506849315, 'recall': 0.819718309859155, 'f1': 0.7819077474249888, 'number': 1065}	0.6961	0.7702	0.7313	0.8007
0.4188	9.0	90	0.6664	{'precision': 0.6888888888888889, 'recall': 0.8046971569839307, 'f1': 0.74230330672748, 'number': 809}	{'precision': 0.2727272727272727, 'recall': 0.25210084033613445, 'f1': 0.26200873362445415, 'number': 119}	{'precision': 0.7708703374777975, 'recall': 0.8150234741784037, 'f1': 0.792332268370607, 'number': 1065}	0.7102	0.7772	0.7422	0.8045
0.3724	10.0	100	0.6845	{'precision': 0.6928721174004193, 'recall': 0.8170580964153276, 'f1': 0.7498581962563812, 'number': 809}	{'precision': 0.33, 'recall': 0.2773109243697479, 'f1': 0.30136986301369867, 'number': 119}	{'precision': 0.7818343722172751, 'recall': 0.8244131455399061, 'f1': 0.8025594149908593, 'number': 1065}	0.7221	0.7888	0.7540	0.8047
0.3402	11.0	110	0.6830	{'precision': 0.7118093174431203, 'recall': 0.8121137206427689, 'f1': 0.7586605080831409, 'number': 809}	{'precision': 0.3090909090909091, 'recall': 0.2857142857142857, 'f1': 0.296943231441048, 'number': 119}	{'precision': 0.787422497785651, 'recall': 0.8347417840375587, 'f1': 0.8103919781221514, 'number': 1065}	0.7308	0.7928	0.7605	0.8129
0.3219	12.0	120	0.6944	{'precision': 0.7179203539823009, 'recall': 0.8022249690976514, 'f1': 0.7577349678925861, 'number': 809}	{'precision': 0.3220338983050847, 'recall': 0.31932773109243695, 'f1': 0.32067510548523204, 'number': 119}	{'precision': 0.781882145998241, 'recall': 0.8347417840375587, 'f1': 0.8074477747502271, 'number': 1065}	0.7300	0.7908	0.7592	0.8097
0.3004	13.0	130	0.6978	{'precision': 0.7147540983606557, 'recall': 0.8084054388133498, 'f1': 0.7587006960556845, 'number': 809}	{'precision': 0.33043478260869563, 'recall': 0.31932773109243695, 'f1': 0.32478632478632474, 'number': 119}	{'precision': 0.7890974084003575, 'recall': 0.8291079812206573, 'f1': 0.8086080586080587, 'number': 1065}	0.7329	0.7903	0.7605	0.8144
0.2942	14.0	140	0.7001	{'precision': 0.7145945945945946, 'recall': 0.8170580964153276, 'f1': 0.7623990772779701, 'number': 809}	{'precision': 0.30708661417322836, 'recall': 0.3277310924369748, 'f1': 0.3170731707317073, 'number': 119}	{'precision': 0.7820284697508897, 'recall': 0.8253521126760563, 'f1': 0.8031064412973961, 'number': 1065}	0.7256	0.7923	0.7575	0.8108
0.2853	15.0	150	0.7024	{'precision': 0.7113513513513513, 'recall': 0.8133498145859085, 'f1': 0.7589388696655133, 'number': 809}	{'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}	{'precision': 0.7811387900355872, 'recall': 0.8244131455399061, 'f1': 0.8021927820922796, 'number': 1065}	0.7241	0.7903	0.7558	0.8106

Framework versions

Transformers 4.25.1
Pytorch 1.13.1+cu117
Datasets 2.7.1
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Dec 16, 2022
Updated: Dec 16, 2022