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lilt-invoices
This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0031
- Endorname: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 177}
- Escription: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 183}
- Illingaddress: {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161}
- Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 175}
- Nitprice: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 156}
- Nvoicedate: {'precision': 0.9941520467836257, 'recall': 1.0, 'f1': 0.9970674486803519, 'number': 170}
- Nvoicetotal: {'precision': 0.9946808510638298, 'recall': 0.9946808510638298, 'f1': 0.9946808510638298, 'number': 188}
- Otaltax: {'precision': 1.0, 'recall': 0.9927007299270073, 'f1': 0.9963369963369962, 'number': 137}
- Uantity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 167}
- Ubtotal: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 151}
- Overall Precision: 0.9988
- Overall Recall: 0.9982
- Overall F1: 0.9985
- Overall Accuracy: 0.9994
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: 500
Training results
| Training Loss | Epoch | Step | Validation Loss | Endorname | Escription | Illingaddress | Mount | Nitprice | Nvoicedate | Nvoicetotal | Otaltax | Uantity | Ubtotal | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1736 | 21.74 | 500 | 0.0031 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 177} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 183} | {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 175} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 156} | {'precision': 0.9941520467836257, 'recall': 1.0, 'f1': 0.9970674486803519, 'number': 170} | {'precision': 0.9946808510638298, 'recall': 0.9946808510638298, 'f1': 0.9946808510638298, 'number': 188} | {'precision': 1.0, 'recall': 0.9927007299270073, 'f1': 0.9963369963369962, 'number': 137} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 167} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 151} | 0.9988 | 0.9982 | 0.9985 | 0.9994 |
Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
