lilt-en-funsd

oec24/lilt-en-funsd

lilt-en-funsd is a token classification model from oec24. 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: May 19, 2023
Updated: May 22, 2023
SHA: de04624d

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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: 1.6055
Answer: {'precision': 0.8728323699421965, 'recall': 0.9241126070991432, 'f1': 0.8977407847800237, 'number': 817}
Header: {'precision': 0.6, 'recall': 0.5042016806722689, 'f1': 0.547945205479452, 'number': 119}
Question: {'precision': 0.9, 'recall': 0.9108635097493036, 'f1': 0.9053991693585604, 'number': 1077}
Overall Precision: 0.8740
Overall Recall: 0.8922
Overall F1: 0.8830
Overall Accuracy: 0.8022

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
0.4096	10.53	200	0.9947	{'precision': 0.8293515358361775, 'recall': 0.8922888616891065, 'f1': 0.8596698113207547, 'number': 817}	{'precision': 0.5833333333333334, 'recall': 0.5294117647058824, 'f1': 0.5550660792951542, 'number': 119}	{'precision': 0.8858195211786372, 'recall': 0.89322191272052, 'f1': 0.8895053166897827, 'number': 1077}	0.8461	0.8713	0.8585	0.8133
0.0451	21.05	400	1.4850	{'precision': 0.8394495412844036, 'recall': 0.8959608323133414, 'f1': 0.866785079928952, 'number': 817}	{'precision': 0.6333333333333333, 'recall': 0.4789915966386555, 'f1': 0.5454545454545454, 'number': 119}	{'precision': 0.9026974951830443, 'recall': 0.8700092850510678, 'f1': 0.8860520094562647, 'number': 1077}	0.863	0.8574	0.8602	0.7910
0.0147	31.58	600	1.5603	{'precision': 0.8478260869565217, 'recall': 0.9069767441860465, 'f1': 0.8764044943820224, 'number': 817}	{'precision': 0.6336633663366337, 'recall': 0.5378151260504201, 'f1': 0.5818181818181819, 'number': 119}	{'precision': 0.8845807033363391, 'recall': 0.9108635097493036, 'f1': 0.8975297346752059, 'number': 1077}	0.8570	0.8872	0.8719	0.7948
0.0077	42.11	800	1.7433	{'precision': 0.8344444444444444, 'recall': 0.9192166462668299, 'f1': 0.8747815958066395, 'number': 817}	{'precision': 0.5596330275229358, 'recall': 0.5126050420168067, 'f1': 0.5350877192982455, 'number': 119}	{'precision': 0.9031954887218046, 'recall': 0.8922934076137419, 'f1': 0.897711349836525, 'number': 1077}	0.8553	0.8808	0.8678	0.7910
0.004	52.63	1000	1.6055	{'precision': 0.8728323699421965, 'recall': 0.9241126070991432, 'f1': 0.8977407847800237, 'number': 817}	{'precision': 0.6, 'recall': 0.5042016806722689, 'f1': 0.547945205479452, 'number': 119}	{'precision': 0.9, 'recall': 0.9108635097493036, 'f1': 0.9053991693585604, 'number': 1077}	0.8740	0.8922	0.8830	0.8022
0.0021	63.16	1200	1.7317	{'precision': 0.8688524590163934, 'recall': 0.9082007343941249, 'f1': 0.8880909634949131, 'number': 817}	{'precision': 0.5904761904761905, 'recall': 0.5210084033613446, 'f1': 0.5535714285714286, 'number': 119}	{'precision': 0.8847184986595175, 'recall': 0.9192200557103064, 'f1': 0.9016393442622952, 'number': 1077}	0.8633	0.8912	0.8770	0.7979
0.0017	73.68	1400	1.7249	{'precision': 0.8705463182897862, 'recall': 0.8971848225214198, 'f1': 0.8836648583484027, 'number': 817}	{'precision': 0.5555555555555556, 'recall': 0.5042016806722689, 'f1': 0.5286343612334802, 'number': 119}	{'precision': 0.8818181818181818, 'recall': 0.9006499535747446, 'f1': 0.891134588883785, 'number': 1077}	0.86	0.8758	0.8678	0.8014
0.0012	84.21	1600	1.8716	{'precision': 0.8679678530424799, 'recall': 0.9253365973072215, 'f1': 0.8957345971563981, 'number': 817}	{'precision': 0.5652173913043478, 'recall': 0.5462184873949579, 'f1': 0.5555555555555555, 'number': 119}	{'precision': 0.898320895522388, 'recall': 0.8941504178272981, 'f1': 0.8962308050255934, 'number': 1077}	0.8669	0.8862	0.8764	0.7946
0.0006	94.74	1800	1.8381	{'precision': 0.8563218390804598, 'recall': 0.9118727050183598, 'f1': 0.8832246591582691, 'number': 817}	{'precision': 0.6055045871559633, 'recall': 0.5546218487394958, 'f1': 0.5789473684210525, 'number': 119}	{'precision': 0.8931226765799256, 'recall': 0.8922934076137419, 'f1': 0.8927078495123084, 'number': 1077}	0.8623	0.8803	0.8712	0.7954
0.0007	105.26	2000	1.7090	{'precision': 0.8822115384615384, 'recall': 0.8984088127294981, 'f1': 0.8902365069739235, 'number': 817}	{'precision': 0.576271186440678, 'recall': 0.5714285714285714, 'f1': 0.5738396624472574, 'number': 119}	{'precision': 0.8811881188118812, 'recall': 0.9090064995357474, 'f1': 0.8948811700182815, 'number': 1077}	0.8641	0.8847	0.8743	0.8122
0.0003	115.79	2200	1.7487	{'precision': 0.8730723606168446, 'recall': 0.9008567931456548, 'f1': 0.8867469879518072, 'number': 817}	{'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119}	{'precision': 0.8936755270394133, 'recall': 0.9052924791086351, 'f1': 0.8994464944649446, 'number': 1077}	0.8654	0.8847	0.8750	0.8084
0.0002	126.32	2400	1.7644	{'precision': 0.8686046511627907, 'recall': 0.9143206854345165, 'f1': 0.8908765652951699, 'number': 817}	{'precision': 0.5932203389830508, 'recall': 0.5882352941176471, 'f1': 0.5907172995780592, 'number': 119}	{'precision': 0.8961397058823529, 'recall': 0.9052924791086351, 'f1': 0.9006928406466513, 'number': 1077}	0.8674	0.8902	0.8786	0.8091

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: May 19, 2023
Updated: May 22, 2023