lilt-en-funsd

moisesmota/lilt-en-funsd

lilt-en-funsd is a token classification model from moisesmota. 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. --

Downloads · 30 days

2% of all-time downloads

All-time downloads

283

Public

Repo size

6.8 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin521 MB · 99%

At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: lilt
Access: Public
Created: May 2, 2023
Updated: May 2, 2023
SHA: 95a2067c

Try a prompt

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: 1.4114
Answer: {'precision': 0.8497175141242937, 'recall': 0.9204406364749081, 'f1': 0.8836662749706228, 'number': 817}
Header: {'precision': 0.6534653465346535, 'recall': 0.5546218487394958, 'f1': 0.6000000000000001, 'number': 119}
Question: {'precision': 0.8935018050541517, 'recall': 0.9192200557103064, 'f1': 0.9061784897025171, 'number': 1077}
Overall Precision: 0.8634
Overall Recall: 0.8982
Overall F1: 0.8804
Overall Accuracy: 0.8253

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: 4
eval_batch_size: 4
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
0.575	5.26	200	0.8531	{'precision': 0.7890295358649789, 'recall': 0.9155446756425949, 'f1': 0.8475920679886686, 'number': 817}	{'precision': 0.5416666666666666, 'recall': 0.3277310924369748, 'f1': 0.40837696335078527, 'number': 119}	{'precision': 0.8611599297012302, 'recall': 0.9099350046425255, 'f1': 0.8848758465011286, 'number': 1077}	0.8188	0.8778	0.8473	0.7926
0.119	10.53	400	1.1026	{'precision': 0.8278688524590164, 'recall': 0.8653610771113831, 'f1': 0.846199880311191, 'number': 817}	{'precision': 0.5813953488372093, 'recall': 0.42016806722689076, 'f1': 0.48780487804878053, 'number': 119}	{'precision': 0.856655290102389, 'recall': 0.9322191272051996, 'f1': 0.8928412627834592, 'number': 1077}	0.8338	0.8748	0.8538	0.8124
0.0411	15.79	600	1.2238	{'precision': 0.8713942307692307, 'recall': 0.8873929008567931, 'f1': 0.8793208004851426, 'number': 817}	{'precision': 0.552, 'recall': 0.5798319327731093, 'f1': 0.5655737704918032, 'number': 119}	{'precision': 0.8669527896995708, 'recall': 0.9377901578458682, 'f1': 0.9009812667261373, 'number': 1077}	0.8501	0.8962	0.8726	0.8131
0.0186	21.05	800	1.2807	{'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817}	{'precision': 0.5447154471544715, 'recall': 0.5630252100840336, 'f1': 0.5537190082644629, 'number': 119}	{'precision': 0.8921389396709324, 'recall': 0.9062209842154132, 'f1': 0.8991248272685398, 'number': 1077}	0.8586	0.8867	0.8724	0.8162
0.0098	26.32	1000	1.3494	{'precision': 0.852233676975945, 'recall': 0.9106487148102815, 'f1': 0.8804733727810652, 'number': 817}	{'precision': 0.5511811023622047, 'recall': 0.5882352941176471, 'f1': 0.5691056910569106, 'number': 119}	{'precision': 0.8794964028776978, 'recall': 0.9080779944289693, 'f1': 0.8935587026039287, 'number': 1077}	0.8485	0.8902	0.8688	0.8039
0.0068	31.58	1200	1.3878	{'precision': 0.8495475113122172, 'recall': 0.9192166462668299, 'f1': 0.8830099941211051, 'number': 817}	{'precision': 0.5565217391304348, 'recall': 0.5378151260504201, 'f1': 0.547008547008547, 'number': 119}	{'precision': 0.899624765478424, 'recall': 0.8904363974001857, 'f1': 0.8950069995333644, 'number': 1077}	0.8591	0.8813	0.8700	0.8140
0.0056	36.84	1400	1.4679	{'precision': 0.8338833883388339, 'recall': 0.9277845777233782, 'f1': 0.8783314020857474, 'number': 817}	{'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119}	{'precision': 0.8971000935453695, 'recall': 0.8904363974001857, 'f1': 0.8937558247903075, 'number': 1077}	0.8569	0.8862	0.8713	0.8117
0.0033	42.11	1600	1.3959	{'precision': 0.8463276836158192, 'recall': 0.9167686658506732, 'f1': 0.8801410105757932, 'number': 817}	{'precision': 0.5833333333333334, 'recall': 0.5882352941176471, 'f1': 0.5857740585774059, 'number': 119}	{'precision': 0.8939114391143912, 'recall': 0.8997214484679665, 'f1': 0.8968070337806571, 'number': 1077}	0.8559	0.8882	0.8718	0.8177
0.0013	47.37	1800	1.4114	{'precision': 0.8497175141242937, 'recall': 0.9204406364749081, 'f1': 0.8836662749706228, 'number': 817}	{'precision': 0.6534653465346535, 'recall': 0.5546218487394958, 'f1': 0.6000000000000001, 'number': 119}	{'precision': 0.8935018050541517, 'recall': 0.9192200557103064, 'f1': 0.9061784897025171, 'number': 1077}	0.8634	0.8982	0.8804	0.8253
0.001	52.63	2000	1.3795	{'precision': 0.8584795321637427, 'recall': 0.8984088127294981, 'f1': 0.8779904306220095, 'number': 817}	{'precision': 0.6306306306306306, 'recall': 0.5882352941176471, 'f1': 0.6086956521739131, 'number': 119}	{'precision': 0.8965201465201466, 'recall': 0.9090064995357474, 'f1': 0.9027201475334256, 'number': 1077}	0.8664	0.8857	0.8760	0.8339
0.0007	57.89	2200	1.4095	{'precision': 0.8586206896551725, 'recall': 0.9143206854345165, 'f1': 0.8855957320687612, 'number': 817}	{'precision': 0.6055045871559633, 'recall': 0.5546218487394958, 'f1': 0.5789473684210525, 'number': 119}	{'precision': 0.8887884267631103, 'recall': 0.9127205199628597, 'f1': 0.9005955107650022, 'number': 1077}	0.8614	0.8922	0.8765	0.8216
0.0006	63.16	2400	1.4001	{'precision': 0.8577981651376146, 'recall': 0.9155446756425949, 'f1': 0.8857312018946123, 'number': 817}	{'precision': 0.6216216216216216, 'recall': 0.5798319327731093, 'f1': 0.6000000000000001, 'number': 119}	{'precision': 0.895644283121597, 'recall': 0.9164345403899722, 'f1': 0.9059201468563561, 'number': 1077}	0.8652	0.8962	0.8804	0.8282

Framework versions

Transformers 4.28.1
Pytorch 1.13.0+cu117
Datasets 2.11.0
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: May 2, 2023
Updated: May 2, 2023