lilt-en-funsd

ssakha/lilt-en-funsd

lilt-en-funsd is a token classification model from ssakha. 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: Apr 3, 2024
Updated: Apr 3, 2024
SHA: 597aa6ea

Try a prompt

Base models

SCUT-DLVCLab/lilt-roberta-en-base

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 an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.4190
Answer: {'precision': 0.8649592549476135, 'recall': 0.9094247246022031, 'f1': 0.886634844868735, 'number': 817}
Header: {'precision': 0.6597938144329897, 'recall': 0.5378151260504201, 'f1': 0.5925925925925926, 'number': 119}
Question: {'precision': 0.8840970350404312, 'recall': 0.9136490250696379, 'f1': 0.8986301369863015, 'number': 1077}
Overall Precision: 0.8656
Overall Recall: 0.8897
Overall F1: 0.8775
Overall Accuracy: 0.8041

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: 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
0.4199	10.53	200	1.0303	{'precision': 0.8458904109589042, 'recall': 0.9069767441860465, 'f1': 0.8753691671588896, 'number': 817}	{'precision': 0.4470588235294118, 'recall': 0.6386554621848739, 'f1': 0.5259515570934256, 'number': 119}	{'precision': 0.8797653958944281, 'recall': 0.8356545961002786, 'f1': 0.8571428571428572, 'number': 1077}	0.8299	0.8530	0.8413	0.7784
0.0485	21.05	400	1.3395	{'precision': 0.8127705627705628, 'recall': 0.9192166462668299, 'f1': 0.8627225732337737, 'number': 817}	{'precision': 0.6463414634146342, 'recall': 0.44537815126050423, 'f1': 0.527363184079602, 'number': 119}	{'precision': 0.8720292504570384, 'recall': 0.8857938718662952, 'f1': 0.878857669276831, 'number': 1077}	0.8371	0.8733	0.8549	0.7851
0.0154	31.58	600	1.2980	{'precision': 0.8578034682080925, 'recall': 0.9082007343941249, 'f1': 0.8822829964328182, 'number': 817}	{'precision': 0.5742574257425742, 'recall': 0.48739495798319327, 'f1': 0.5272727272727273, 'number': 119}	{'precision': 0.87322695035461, 'recall': 0.914577530176416, 'f1': 0.8934240362811792, 'number': 1077}	0.8524	0.8867	0.8692	0.8145
0.0076	42.11	800	1.3862	{'precision': 0.8296703296703297, 'recall': 0.9241126070991432, 'f1': 0.8743485813549509, 'number': 817}	{'precision': 0.6206896551724138, 'recall': 0.453781512605042, 'f1': 0.5242718446601942, 'number': 119}	{'precision': 0.8699472759226714, 'recall': 0.9192200557103064, 'f1': 0.8939051918735892, 'number': 1077}	0.8426	0.8937	0.8674	0.8016
0.0055	52.63	1000	1.4190	{'precision': 0.8649592549476135, 'recall': 0.9094247246022031, 'f1': 0.886634844868735, 'number': 817}	{'precision': 0.6597938144329897, 'recall': 0.5378151260504201, 'f1': 0.5925925925925926, 'number': 119}	{'precision': 0.8840970350404312, 'recall': 0.9136490250696379, 'f1': 0.8986301369863015, 'number': 1077}	0.8656	0.8897	0.8775	0.8041
0.0026	63.16	1200	1.5891	{'precision': 0.8293478260869566, 'recall': 0.9339045287637698, 'f1': 0.8785261945883708, 'number': 817}	{'precision': 0.5922330097087378, 'recall': 0.5126050420168067, 'f1': 0.5495495495495496, 'number': 119}	{'precision': 0.9082217973231358, 'recall': 0.8820798514391829, 'f1': 0.8949599623174752, 'number': 1077}	0.8574	0.8813	0.8692	0.8058
0.0027	73.68	1400	1.6258	{'precision': 0.8331466965285554, 'recall': 0.9106487148102815, 'f1': 0.8701754385964913, 'number': 817}	{'precision': 0.5833333333333334, 'recall': 0.5294117647058824, 'f1': 0.5550660792951542, 'number': 119}	{'precision': 0.8783542039355993, 'recall': 0.9117920148560817, 'f1': 0.894760820045558, 'number': 1077}	0.8443	0.8887	0.8659	0.7927
0.0009	84.21	1600	1.6324	{'precision': 0.8621495327102804, 'recall': 0.9033047735618115, 'f1': 0.8822474596533174, 'number': 817}	{'precision': 0.6, 'recall': 0.5294117647058824, 'f1': 0.5625, 'number': 119}	{'precision': 0.8733153638814016, 'recall': 0.9025069637883009, 'f1': 0.8876712328767123, 'number': 1077}	0.8549	0.8808	0.8676	0.7950
0.0007	94.74	1800	1.8058	{'precision': 0.8278145695364238, 'recall': 0.9179926560587516, 'f1': 0.8705745792222868, 'number': 817}	{'precision': 0.5714285714285714, 'recall': 0.5042016806722689, 'f1': 0.5357142857142857, 'number': 119}	{'precision': 0.9029495718363464, 'recall': 0.8811513463324049, 'f1': 0.8919172932330827, 'number': 1077}	0.8531	0.8738	0.8633	0.7927
0.0005	105.26	2000	1.8281	{'precision': 0.8543799772468714, 'recall': 0.9192166462668299, 'f1': 0.8856132075471698, 'number': 817}	{'precision': 0.5238095238095238, 'recall': 0.5546218487394958, 'f1': 0.5387755102040817, 'number': 119}	{'precision': 0.9050279329608939, 'recall': 0.9025069637883009, 'f1': 0.903765690376569, 'number': 1077}	0.8605	0.8887	0.8744	0.7915
0.0004	115.79	2200	1.6623	{'precision': 0.8643867924528302, 'recall': 0.8971848225214198, 'f1': 0.8804804804804804, 'number': 817}	{'precision': 0.5426356589147286, 'recall': 0.5882352941176471, 'f1': 0.5645161290322581, 'number': 119}	{'precision': 0.8898916967509025, 'recall': 0.9155060352831941, 'f1': 0.9025171624713959, 'number': 1077}	0.8580	0.8887	0.8731	0.8055
0.0003	126.32	2400	1.7066	{'precision': 0.8649592549476135, 'recall': 0.9094247246022031, 'f1': 0.886634844868735, 'number': 817}	{'precision': 0.5689655172413793, 'recall': 0.5546218487394958, 'f1': 0.5617021276595745, 'number': 119}	{'precision': 0.8988970588235294, 'recall': 0.9080779944289693, 'f1': 0.9034642032332563, 'number': 1077}	0.8662	0.8877	0.8768	0.8010

Framework versions

Transformers 4.39.3
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: Apr 3, 2024
Updated: Apr 3, 2024