metadata

license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
  - generated_from_trainer
model-index:
  - name: lilt-en-funsd
    results: []

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