lilt-en-funsd-custom

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the mydata dataset. It achieves the following results on the evaluation set:

• Loss: 0.0023
• In: {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2}
• Ear: {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2}
• Overall Precision: 0.6
• Overall Recall: 0.75
• Overall F1: 0.6667
• Overall Accuracy: 0.9984

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: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 200
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	In	Ear	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.1628	25.0	50	0.0023	{'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2}	{'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2}	0.6	0.75	0.6667	0.9984
0.0002	50.0	100	0.0015	{'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2}	{'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2}	0.6	0.75	0.6667	0.9984
0.0001	75.0	150	0.0020	{'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2}	{'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2}	0.6	0.75	0.6667	0.9984
0.0	100.0	200	0.0020	{'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2}	{'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2}	0.6	0.75	0.6667	0.9984

Framework versions

• Transformers 4.30.0.dev0
• Pytorch 1.8.0+cu101
• Datasets 2.12.0
• Tokenizers 0.13.3