layoutlm-funsd

nishthaahuja25/layoutlm-funsd

layoutlm-funsd is a token classification model from nishthaahuja25. 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. --

Downloads · 30 days

8% of all-time downloads

All-time downloads

Public

Repo size

5.9 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin451 MB · 100%

At a glance

Task: Token Classification
Library: transformers
Model type: layoutlm
Access: Public
Created: Oct 26, 2023
Updated: Oct 26, 2023
SHA: b3d72644

Try a prompt

Base models

microsoft/layoutlm-base-uncased

Datasets

funsd

Model card

From the Hugging Face model README

layoutlm-funsd

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: 0.7107
Answer: {'precision': 0.696078431372549, 'recall': 0.7898640296662547, 'f1': 0.740011580775912, 'number': 809}
Header: {'precision': 0.3333333333333333, 'recall': 0.3277310924369748, 'f1': 0.3305084745762712, 'number': 119}
Question: {'precision': 0.7799827437446074, 'recall': 0.8488262910798122, 'f1': 0.8129496402877696, 'number': 1065}
Overall Precision: 0.7211
Overall Recall: 0.7938
Overall F1: 0.7557
Overall Accuracy: 0.8008

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

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.8202	1.0	10	1.5835	{'precision': 0.025120772946859903, 'recall': 0.032138442521631644, 'f1': 0.028199566160520606, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.20365535248041775, 'recall': 0.21971830985915494, 'f1': 0.2113821138211382, 'number': 1065}	0.1190	0.1305	0.1245	0.3882
1.4316	2.0	20	1.2683	{'precision': 0.16164383561643836, 'recall': 0.14585908529048208, 'f1': 0.1533463287849253, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4461190655614167, 'recall': 0.5558685446009389, 'f1': 0.49498327759197325, 'number': 1065}	0.3452	0.3562	0.3506	0.5544
1.1076	3.0	30	0.9812	{'precision': 0.44790547798066593, 'recall': 0.515451174289246, 'f1': 0.4793103448275862, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.5667215815485996, 'recall': 0.6460093896713615, 'f1': 0.6037735849056605, 'number': 1065}	0.5087	0.5544	0.5306	0.6776
0.845	4.0	40	0.8144	{'precision': 0.5346083788706739, 'recall': 0.7255871446229913, 'f1': 0.615626638699528, 'number': 809}	{'precision': 0.029411764705882353, 'recall': 0.01680672268907563, 'f1': 0.021390374331550797, 'number': 119}	{'precision': 0.6285482562854826, 'recall': 0.7276995305164319, 'f1': 0.6744995648389904, 'number': 1065}	0.5686	0.6844	0.6211	0.7435
0.6785	5.0	50	0.7277	{'precision': 0.6069651741293532, 'recall': 0.754017305315204, 'f1': 0.6725468577728776, 'number': 809}	{'precision': 0.29850746268656714, 'recall': 0.16806722689075632, 'f1': 0.21505376344086022, 'number': 119}	{'precision': 0.6952296819787986, 'recall': 0.7389671361502348, 'f1': 0.7164314974965864, 'number': 1065}	0.6429	0.7110	0.6752	0.7748
0.5677	6.0	60	0.6958	{'precision': 0.6477987421383647, 'recall': 0.7639060568603214, 'f1': 0.7010777084515031, 'number': 809}	{'precision': 0.29347826086956524, 'recall': 0.226890756302521, 'f1': 0.2559241706161137, 'number': 119}	{'precision': 0.7031375703942075, 'recall': 0.8206572769953052, 'f1': 0.7573656845753899, 'number': 1065}	0.6636	0.7622	0.7095	0.7865
0.4873	7.0	70	0.6717	{'precision': 0.6487046632124353, 'recall': 0.7737948084054388, 'f1': 0.705749718151071, 'number': 809}	{'precision': 0.31313131313131315, 'recall': 0.2605042016806723, 'f1': 0.28440366972477066, 'number': 119}	{'precision': 0.7402707275803723, 'recall': 0.8215962441314554, 'f1': 0.778816199376947, 'number': 1065}	0.6821	0.7687	0.7228	0.7916
0.4374	8.0	80	0.6668	{'precision': 0.6781115879828327, 'recall': 0.7812113720642769, 'f1': 0.7260195290063183, 'number': 809}	{'precision': 0.3173076923076923, 'recall': 0.2773109243697479, 'f1': 0.29596412556053814, 'number': 119}	{'precision': 0.743142144638404, 'recall': 0.8394366197183099, 'f1': 0.7883597883597885, 'number': 1065}	0.6963	0.7822	0.7368	0.7951
0.3894	9.0	90	0.6763	{'precision': 0.6646341463414634, 'recall': 0.8084054388133498, 'f1': 0.7295036252091467, 'number': 809}	{'precision': 0.3465346534653465, 'recall': 0.29411764705882354, 'f1': 0.3181818181818182, 'number': 119}	{'precision': 0.7589833479404031, 'recall': 0.8131455399061033, 'f1': 0.7851314596554851, 'number': 1065}	0.6986	0.7802	0.7371	0.7977
0.3492	10.0	100	0.6727	{'precision': 0.6944140197152245, 'recall': 0.7836835599505563, 'f1': 0.7363530778164925, 'number': 809}	{'precision': 0.32231404958677684, 'recall': 0.3277310924369748, 'f1': 0.32499999999999996, 'number': 119}	{'precision': 0.762350936967632, 'recall': 0.8403755868544601, 'f1': 0.7994640464493078, 'number': 1065}	0.7101	0.7868	0.7465	0.7993
0.3166	11.0	110	0.6756	{'precision': 0.6956055734190782, 'recall': 0.8022249690976514, 'f1': 0.7451205510907002, 'number': 809}	{'precision': 0.30833333333333335, 'recall': 0.31092436974789917, 'f1': 0.3096234309623431, 'number': 119}	{'precision': 0.7672047578589635, 'recall': 0.847887323943662, 'f1': 0.8055307760927745, 'number': 1065}	0.7126	0.7973	0.7525	0.8022
0.2962	12.0	120	0.7018	{'precision': 0.6983783783783784, 'recall': 0.7985166872682324, 'f1': 0.7450980392156863, 'number': 809}	{'precision': 0.32231404958677684, 'recall': 0.3277310924369748, 'f1': 0.32499999999999996, 'number': 119}	{'precision': 0.7716468590831919, 'recall': 0.8535211267605634, 'f1': 0.8105216228265715, 'number': 1065}	0.7167	0.7998	0.7560	0.8007
0.2861	13.0	130	0.7136	{'precision': 0.6987041036717062, 'recall': 0.799752781211372, 'f1': 0.745821325648415, 'number': 809}	{'precision': 0.3305084745762712, 'recall': 0.3277310924369748, 'f1': 0.32911392405063294, 'number': 119}	{'precision': 0.7859007832898173, 'recall': 0.847887323943662, 'f1': 0.8157181571815718, 'number': 1065}	0.7246	0.7973	0.7592	0.8021
0.2685	14.0	140	0.7143	{'precision': 0.6964091403699674, 'recall': 0.7911001236093943, 'f1': 0.7407407407407407, 'number': 809}	{'precision': 0.325, 'recall': 0.3277310924369748, 'f1': 0.3263598326359833, 'number': 119}	{'precision': 0.7802768166089965, 'recall': 0.8469483568075117, 'f1': 0.8122467357046375, 'number': 1065}	0.7203	0.7933	0.7550	0.8003
0.2625	15.0	150	0.7107	{'precision': 0.696078431372549, 'recall': 0.7898640296662547, 'f1': 0.740011580775912, 'number': 809}	{'precision': 0.3333333333333333, 'recall': 0.3277310924369748, 'f1': 0.3305084745762712, 'number': 119}	{'precision': 0.7799827437446074, 'recall': 0.8488262910798122, 'f1': 0.8129496402877696, 'number': 1065}	0.7211	0.7938	0.7557	0.8008

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Oct 26, 2023
Updated: Oct 26, 2023