End of training

README.md

@@ -15,21 +15,21 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1475
-- Endorname: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
-- Escription: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
-- Illingaddress: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
-- Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
-- Nitprice: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}
-- Nvoicedate: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
-- Nvoicetotal: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
-- Otaltax: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
-- Uantity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
-- Ubtotal: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
-- Overall Precision: 1.0
-- Overall Recall: 1.0
-- Overall F1: 1.0
-- Overall Accuracy: 1.0
+- 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
 
@@ -54,15 +54,18 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- training_steps: 20
+- 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 4.31.0
+- Transformers 4.32.0
 - Pytorch 2.0.1+cu118
 - Datasets 2.14.4
 - Tokenizers 0.13.3