End of training

README.md

@@ -14,15 +14,15 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [NlpHUST/ner-vietnamese-electra-base](https://huggingface.co/NlpHUST/ner-vietnamese-electra-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0681
-- Location: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7}
-- Miscellaneous: {'precision': 0.6911764705882353, 'recall': 0.7230769230769231, 'f1': 0.7067669172932332, 'number': 65}
-- Organization: {'precision': 0.4166666666666667, 'recall': 0.5, 'f1': 0.45454545454545453, 'number': 10}
-- Person: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
-- Overall Precision: 0.6190
-- Overall Recall: 0.6118
-- Overall F1: 0.6154
-- Overall Accuracy: 0.9851
+- Loss: 0.1569
+- Location: {'precision': 0.8888888888888888, 'recall': 0.6153846153846154, 'f1': 0.7272727272727274, 'number': 13}
+- Miscellaneous: {'precision': 0.6255506607929515, 'recall': 0.7029702970297029, 'f1': 0.662004662004662, 'number': 202}
+- Organization: {'precision': 0.723404255319149, 'recall': 0.7906976744186046, 'f1': 0.7555555555555555, 'number': 43}
+- Person: {'precision': 0.3157894736842105, 'recall': 0.3157894736842105, 'f1': 0.3157894736842105, 'number': 19}
+- Overall Precision: 0.6291
+- Overall Recall: 0.6859
+- Overall F1: 0.6563
+- Overall Accuracy: 0.9587
 
 ## Model description
 
@@ -51,16 +51,16 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Location                                                  | Miscellaneous                                                                                            | Organization                                                                              | Person                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| No log        | 1.0   | 207  | 0.0705          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.49333333333333335, 'recall': 0.5692307692307692, 'f1': 0.5285714285714285, 'number': 65} | {'precision': 0.36363636363636365, 'recall': 0.4, 'f1': 0.380952380952381, 'number': 10}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.4556            | 0.4824         | 0.4686     | 0.9806           |
-| No log        | 2.0   | 414  | 0.0672          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.6133333333333333, 'recall': 0.7076923076923077, 'f1': 0.657142857142857, 'number': 65}   | {'precision': 0.4666666666666667, 'recall': 0.7, 'f1': 0.56, 'number': 10}                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.5638            | 0.6235         | 0.5922     | 0.9838           |
-| 0.0564        | 3.0   | 621  | 0.0681          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.6911764705882353, 'recall': 0.7230769230769231, 'f1': 0.7067669172932332, 'number': 65}  | {'precision': 0.4166666666666667, 'recall': 0.5, 'f1': 0.45454545454545453, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.6190            | 0.6118         | 0.6154     | 0.9851           |
+| Training Loss | Epoch | Step | Validation Loss | Location                                                                                                 | Miscellaneous                                                                                            | Organization                                                                                            | Person                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| No log        | 1.0   | 220  | 0.1317          | {'precision': 0.6666666666666666, 'recall': 0.46153846153846156, 'f1': 0.5454545454545455, 'number': 13} | {'precision': 0.5213675213675214, 'recall': 0.6039603960396039, 'f1': 0.5596330275229358, 'number': 202} | {'precision': 0.38028169014084506, 'recall': 0.627906976744186, 'f1': 0.4736842105263158, 'number': 43} | {'precision': 0.23809523809523808, 'recall': 0.2631578947368421, 'f1': 0.25, 'number': 19}              | 0.4776            | 0.5776         | 0.5229     | 0.9504           |
+| No log        | 2.0   | 440  | 0.1513          | {'precision': 0.6, 'recall': 0.46153846153846156, 'f1': 0.5217391304347826, 'number': 13}                | {'precision': 0.6008403361344538, 'recall': 0.7079207920792079, 'f1': 0.65, 'number': 202}               | {'precision': 0.6538461538461539, 'recall': 0.7906976744186046, 'f1': 0.7157894736842104, 'number': 43} | {'precision': 0.3333333333333333, 'recall': 0.3157894736842105, 'f1': 0.3243243243243243, 'number': 19} | 0.5943            | 0.6823         | 0.6353     | 0.9564           |
+| 0.1164        | 3.0   | 660  | 0.1569          | {'precision': 0.8888888888888888, 'recall': 0.6153846153846154, 'f1': 0.7272727272727274, 'number': 13}  | {'precision': 0.6255506607929515, 'recall': 0.7029702970297029, 'f1': 0.662004662004662, 'number': 202}  | {'precision': 0.723404255319149, 'recall': 0.7906976744186046, 'f1': 0.7555555555555555, 'number': 43}  | {'precision': 0.3157894736842105, 'recall': 0.3157894736842105, 'f1': 0.3157894736842105, 'number': 19} | 0.6291            | 0.6859         | 0.6563     | 0.9587           |
 
 
 ### Framework versions
 
 - Transformers 4.42.4
-- Pytorch 2.3.1+cu121
+- Pytorch 2.4.0+cu121
 - Datasets 2.21.0
 - Tokenizers 0.19.1