lilt-en-funsd

chintans/lilt-en-funsd

lilt-en-funsd is a token classification model from chintans. 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 27, 2024
Updated: May 27, 2024
SHA: 892ec0cf

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: 1.6681
Answer: {'precision': 0.8778173190984578, 'recall': 0.9057527539779682, 'f1': 0.891566265060241, 'number': 817}
Header: {'precision': 0.6036036036036037, 'recall': 0.5630252100840336, 'f1': 0.582608695652174, 'number': 119}
Question: {'precision': 0.9024839006439742, 'recall': 0.9108635097493036, 'f1': 0.9066543438077633, 'number': 1077}
Overall Precision: 0.8760
Overall Recall: 0.8882
Overall F1: 0.8821
Overall Accuracy: 0.8030

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
0.431	10.5263	200	1.0598	{'precision': 0.8017718715393134, 'recall': 0.8861689106487148, 'f1': 0.841860465116279, 'number': 817}	{'precision': 0.4228187919463087, 'recall': 0.5294117647058824, 'f1': 0.47014925373134325, 'number': 119}	{'precision': 0.8755935422602089, 'recall': 0.8560817084493965, 'f1': 0.8657276995305164, 'number': 1077}	0.8119	0.8490	0.8300	0.7774
0.0542	21.0526	400	1.2173	{'precision': 0.8382352941176471, 'recall': 0.9069767441860465, 'f1': 0.8712522045855379, 'number': 817}	{'precision': 0.5350877192982456, 'recall': 0.5126050420168067, 'f1': 0.5236051502145922, 'number': 119}	{'precision': 0.8882521489971347, 'recall': 0.8635097493036211, 'f1': 0.8757062146892656, 'number': 1077}	0.8469	0.8604	0.8536	0.8016
0.014	31.5789	600	1.2955	{'precision': 0.8415051311288484, 'recall': 0.9033047735618115, 'f1': 0.8713105076741442, 'number': 817}	{'precision': 0.6210526315789474, 'recall': 0.4957983193277311, 'f1': 0.5514018691588785, 'number': 119}	{'precision': 0.8972477064220183, 'recall': 0.9080779944289693, 'f1': 0.9026303645592985, 'number': 1077}	0.8608	0.8818	0.8712	0.8160
0.0064	42.1053	800	1.2848	{'precision': 0.8696186961869619, 'recall': 0.8653610771113831, 'f1': 0.8674846625766871, 'number': 817}	{'precision': 0.5193798449612403, 'recall': 0.5630252100840336, 'f1': 0.5403225806451614, 'number': 119}	{'precision': 0.858274647887324, 'recall': 0.9052924791086351, 'f1': 0.8811568007230005, 'number': 1077}	0.8417	0.8689	0.8550	0.8222
0.0037	52.6316	1000	1.5983	{'precision': 0.8530751708428246, 'recall': 0.9167686658506732, 'f1': 0.8837758112094395, 'number': 817}	{'precision': 0.5658914728682171, 'recall': 0.6134453781512605, 'f1': 0.5887096774193549, 'number': 119}	{'precision': 0.8946360153256705, 'recall': 0.8672237697307336, 'f1': 0.8807166430928807, 'number': 1077}	0.8562	0.8723	0.8642	0.7916
0.0034	63.1579	1200	1.5936	{'precision': 0.85, 'recall': 0.9155446756425949, 'f1': 0.881555686505598, 'number': 817}	{'precision': 0.5619047619047619, 'recall': 0.4957983193277311, 'f1': 0.5267857142857143, 'number': 119}	{'precision': 0.8912442396313364, 'recall': 0.8978644382544104, 'f1': 0.8945420906567992, 'number': 1077}	0.8570	0.8813	0.8690	0.8102
0.0021	73.6842	1400	1.4765	{'precision': 0.8558139534883721, 'recall': 0.9008567931456548, 'f1': 0.877757901013715, 'number': 817}	{'precision': 0.5619047619047619, 'recall': 0.4957983193277311, 'f1': 0.5267857142857143, 'number': 119}	{'precision': 0.885036496350365, 'recall': 0.9006499535747446, 'f1': 0.892774965485504, 'number': 1077}	0.8564	0.8768	0.8665	0.8010
0.0009	84.2105	1600	1.6681	{'precision': 0.8778173190984578, 'recall': 0.9057527539779682, 'f1': 0.891566265060241, 'number': 817}	{'precision': 0.6036036036036037, 'recall': 0.5630252100840336, 'f1': 0.582608695652174, 'number': 119}	{'precision': 0.9024839006439742, 'recall': 0.9108635097493036, 'f1': 0.9066543438077633, 'number': 1077}	0.8760	0.8882	0.8821	0.8030
0.0003	94.7368	1800	1.6379	{'precision': 0.8595238095238096, 'recall': 0.8837209302325582, 'f1': 0.8714544357272178, 'number': 817}	{'precision': 0.5929203539823009, 'recall': 0.5630252100840336, 'f1': 0.5775862068965517, 'number': 119}	{'precision': 0.896709323583181, 'recall': 0.9108635097493036, 'f1': 0.9037309995393827, 'number': 1077}	0.8647	0.8793	0.8719	0.7986
0.0002	105.2632	2000	1.7186	{'precision': 0.8644859813084113, 'recall': 0.9057527539779682, 'f1': 0.8846383741781233, 'number': 817}	{'precision': 0.5675675675675675, 'recall': 0.5294117647058824, 'f1': 0.5478260869565218, 'number': 119}	{'precision': 0.8921658986175115, 'recall': 0.8987929433611885, 'f1': 0.8954671600370029, 'number': 1077}	0.8631	0.8798	0.8713	0.7978
0.0003	115.7895	2200	1.6765	{'precision': 0.8690476190476191, 'recall': 0.8935128518971848, 'f1': 0.8811104405552203, 'number': 817}	{'precision': 0.5726495726495726, 'recall': 0.5630252100840336, 'f1': 0.5677966101694915, 'number': 119}	{'precision': 0.8934802571166207, 'recall': 0.903435468895079, 'f1': 0.8984302862419206, 'number': 1077}	0.8651	0.8793	0.8721	0.8000
0.0003	126.3158	2400	1.7309	{'precision': 0.8817852834740652, 'recall': 0.8947368421052632, 'f1': 0.888213851761847, 'number': 817}	{'precision': 0.5675675675675675, 'recall': 0.5294117647058824, 'f1': 0.5478260869565218, 'number': 119}	{'precision': 0.8914233576642335, 'recall': 0.9071494893221913, 'f1': 0.8992176714219972, 'number': 1077}	0.8698	0.8798	0.8748	0.7959

Framework versions

Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: May 27, 2024
Updated: May 27, 2024