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: 0.1569
-- 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
+- Loss: 0.0985
+- Location: {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6}
+- Miscellaneous: {'precision': 0.6069651741293532, 'recall': 0.7176470588235294, 'f1': 0.6576819407008085, 'number': 170}
+- Organization: {'precision': 0.4166666666666667, 'recall': 0.5769230769230769, 'f1': 0.48387096774193544, 'number': 26}
+- Person: {'precision': 0.75, 'recall': 0.6, 'f1': 0.6666666666666665, 'number': 10}
+- Overall Precision: 0.5863
+- Overall Recall: 0.6887
+- Overall F1: 0.6334
+- Overall Accuracy: 0.9702
 
 ## Model description
 
@@ -51,11 +51,11 @@ 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   | 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           |
+| Training Loss | Epoch | Step | Validation Loss | Location                                                                 | Miscellaneous                                                                                             | Organization                                                                                              | Person                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| No log        | 1.0   | 269  | 0.1088          | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6}               | {'precision': 0.46311475409836067, 'recall': 0.6647058823529411, 'f1': 0.5458937198067633, 'number': 170} | {'precision': 0.3333333333333333, 'recall': 0.46153846153846156, 'f1': 0.3870967741935484, 'number': 26}  | {'precision': 0.6666666666666666, 'recall': 0.6, 'f1': 0.631578947368421, 'number': 10} | 0.4573            | 0.6321         | 0.5307     | 0.9631           |
+| 0.1453        | 2.0   | 538  | 0.0948          | {'precision': 1.0, 'recall': 0.5, 'f1': 0.6666666666666666, 'number': 6} | {'precision': 0.5525114155251142, 'recall': 0.711764705882353, 'f1': 0.622107969151671, 'number': 170}    | {'precision': 0.42424242424242425, 'recall': 0.5384615384615384, 'f1': 0.47457627118644075, 'number': 26} | {'precision': 0.75, 'recall': 0.6, 'f1': 0.6666666666666665, 'number': 10}              | 0.5475            | 0.6792         | 0.6063     | 0.9654           |
+| 0.1453        | 3.0   | 807  | 0.0985          | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6}               | {'precision': 0.6069651741293532, 'recall': 0.7176470588235294, 'f1': 0.6576819407008085, 'number': 170}  | {'precision': 0.4166666666666667, 'recall': 0.5769230769230769, 'f1': 0.48387096774193544, 'number': 26}  | {'precision': 0.75, 'recall': 0.6, 'f1': 0.6666666666666665, 'number': 10}              | 0.5863            | 0.6887         | 0.6334     | 0.9702           |
 
 
 ### Framework versions