End of training

README.md

@@ -14,23 +14,23 @@ 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: 0.3314
-- Age: {'precision': 0.8721804511278195, 'recall': 0.8656716417910447, 'f1': 0.8689138576779025, 'number': 134}
-- Datetime: {'precision': 0.6672727272727272, 'recall': 0.7436676798378926, 'f1': 0.7034020124580738, 'number': 987}
-- Disease: {'precision': 0.6857142857142857, 'recall': 0.732824427480916, 'f1': 0.7084870848708487, 'number': 262}
-- Event: {'precision': 0.3482758620689655, 'recall': 0.3607142857142857, 'f1': 0.35438596491228064, 'number': 280}
-- Gender: {'precision': 0.6956521739130435, 'recall': 0.735632183908046, 'f1': 0.7150837988826816, 'number': 87}
-- Law: {'precision': 0.5736677115987461, 'recall': 0.7176470588235294, 'f1': 0.6376306620209058, 'number': 255}
-- Location: {'precision': 0.6905263157894737, 'recall': 0.7309192200557103, 'f1': 0.710148849797023, 'number': 1795}
-- Organization: {'precision': 0.6168493941142528, 'recall': 0.7065432914738929, 'f1': 0.6586568083795441, 'number': 1513}
-- Person: {'precision': 0.6811497326203209, 'recall': 0.7330935251798562, 'f1': 0.7061677061677062, 'number': 1390}
-- Quantity: {'precision': 0.5275229357798165, 'recall': 0.6095406360424028, 'f1': 0.5655737704918032, 'number': 566}
-- Role: {'precision': 0.47716535433070867, 'recall': 0.5539305301645339, 'f1': 0.5126903553299493, 'number': 547}
-- Transportation: {'precision': 0.4962962962962963, 'recall': 0.5826086956521739, 'f1': 0.536, 'number': 115}
-- Overall Precision: 0.6279
-- Overall Recall: 0.6941
-- Overall F1: 0.6594
-- Overall Accuracy: 0.8994
+- Loss: 0.3324
+- Age: {'precision': 0.8854961832061069, 'recall': 0.8656716417910447, 'f1': 0.8754716981132075, 'number': 134}
+- Datetime: {'precision': 0.6675774134790529, 'recall': 0.7426545086119554, 'f1': 0.7031175059952038, 'number': 987}
+- Disease: {'precision': 0.6914893617021277, 'recall': 0.7442748091603053, 'f1': 0.7169117647058824, 'number': 262}
+- Event: {'precision': 0.3287671232876712, 'recall': 0.34285714285714286, 'f1': 0.3356643356643356, 'number': 280}
+- Gender: {'precision': 0.7529411764705882, 'recall': 0.735632183908046, 'f1': 0.7441860465116279, 'number': 87}
+- Law: {'precision': 0.5590062111801242, 'recall': 0.7058823529411765, 'f1': 0.6239168110918544, 'number': 255}
+- Location: {'precision': 0.6794407042982911, 'recall': 0.7309192200557103, 'f1': 0.7042404723564144, 'number': 1795}
+- Organization: {'precision': 0.6267441860465116, 'recall': 0.712491738268341, 'f1': 0.6668728734921126, 'number': 1513}
+- Person: {'precision': 0.6789052069425902, 'recall': 0.7316546762589928, 'f1': 0.7042936288088643, 'number': 1390}
+- Quantity: {'precision': 0.522273425499232, 'recall': 0.6007067137809188, 'f1': 0.5587510271158588, 'number': 566}
+- Role: {'precision': 0.46021840873634945, 'recall': 0.5393053016453382, 'f1': 0.49663299663299665, 'number': 547}
+- Transportation: {'precision': 0.49645390070921985, 'recall': 0.6086956521739131, 'f1': 0.5468749999999999, 'number': 115}
+- Overall Precision: 0.6251
+- Overall Recall: 0.6930
+- Overall F1: 0.6573
+- Overall Accuracy: 0.8992
 
 ## Model description
 
@@ -59,10 +59,10 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch  | Step | Validation Loss | Age                                                                                                      | Datetime                                                                                                 | Disease                                                                                                 | Event                                                                                                      | Gender                                                                                                  | Law                                                                                                      | Location                                                                                                  | Organization                                                                                              | Person                                                                                                    | Quantity                                                                                                 | Role                                                                                                      | Transportation                                                                                          | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.3125        | 1.9991 | 2313 | 0.3301          | {'precision': 0.8740740740740741, 'recall': 0.8805970149253731, 'f1': 0.8773234200743494, 'number': 134} | {'precision': 0.6575591985428051, 'recall': 0.7315096251266464, 'f1': 0.6925659472422062, 'number': 987} | {'precision': 0.6504854368932039, 'recall': 0.767175572519084, 'f1': 0.7040280210157618, 'number': 262} | {'precision': 0.34615384615384615, 'recall': 0.2892857142857143, 'f1': 0.31517509727626464, 'number': 280} | {'precision': 0.7560975609756098, 'recall': 0.7126436781609196, 'f1': 0.7337278106508877, 'number': 87} | {'precision': 0.5468277945619335, 'recall': 0.7098039215686275, 'f1': 0.6177474402730375, 'number': 255} | {'precision': 0.6933625891387822, 'recall': 0.7041782729805014, 'f1': 0.6987285793255943, 'number': 1795} | {'precision': 0.6062176165803109, 'recall': 0.6959682749504296, 'f1': 0.648, 'number': 1513}              | {'precision': 0.70625, 'recall': 0.7316546762589928, 'f1': 0.7187279151943463, 'number': 1390}            | {'precision': 0.5138036809815951, 'recall': 0.5918727915194346, 'f1': 0.5500821018062397, 'number': 566} | {'precision': 0.4824120603015075, 'recall': 0.526508226691042, 'f1': 0.5034965034965035, 'number': 547}   | {'precision': 0.4838709677419355, 'recall': 0.5217391304347826, 'f1': 0.502092050209205, 'number': 115} | 0.6286            | 0.6786         | 0.6526     | 0.9006           |
-| 0.2542        | 3.9983 | 4626 | 0.3314          | {'precision': 0.8721804511278195, 'recall': 0.8656716417910447, 'f1': 0.8689138576779025, 'number': 134} | {'precision': 0.6672727272727272, 'recall': 0.7436676798378926, 'f1': 0.7034020124580738, 'number': 987} | {'precision': 0.6857142857142857, 'recall': 0.732824427480916, 'f1': 0.7084870848708487, 'number': 262} | {'precision': 0.3482758620689655, 'recall': 0.3607142857142857, 'f1': 0.35438596491228064, 'number': 280}  | {'precision': 0.6956521739130435, 'recall': 0.735632183908046, 'f1': 0.7150837988826816, 'number': 87}  | {'precision': 0.5736677115987461, 'recall': 0.7176470588235294, 'f1': 0.6376306620209058, 'number': 255} | {'precision': 0.6905263157894737, 'recall': 0.7309192200557103, 'f1': 0.710148849797023, 'number': 1795}  | {'precision': 0.6168493941142528, 'recall': 0.7065432914738929, 'f1': 0.6586568083795441, 'number': 1513} | {'precision': 0.6811497326203209, 'recall': 0.7330935251798562, 'f1': 0.7061677061677062, 'number': 1390} | {'precision': 0.5275229357798165, 'recall': 0.6095406360424028, 'f1': 0.5655737704918032, 'number': 566} | {'precision': 0.47716535433070867, 'recall': 0.5539305301645339, 'f1': 0.5126903553299493, 'number': 547} | {'precision': 0.4962962962962963, 'recall': 0.5826086956521739, 'f1': 0.536, 'number': 115}             | 0.6279            | 0.6941         | 0.6594     | 0.8994           |
+| Training Loss | Epoch  | Step | Validation Loss | Age                                                                                                      | Datetime                                                                                                 | Disease                                                                                                  | Event                                                                                                       | Gender                                                                                                 | Law                                                                                                      | Location                                                                                                  | Organization                                                                                              | Person                                                                                                    | Quantity                                                                                                | Role                                                                                                       | Transportation                                                                                            | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.3138        | 1.9991 | 2313 | 0.3302          | {'precision': 0.8721804511278195, 'recall': 0.8656716417910447, 'f1': 0.8689138576779025, 'number': 134} | {'precision': 0.6596715328467153, 'recall': 0.7325227963525835, 'f1': 0.6941910705712914, 'number': 987} | {'precision': 0.6421725239616614, 'recall': 0.767175572519084, 'f1': 0.6991304347826088, 'number': 262}  | {'precision': 0.34297520661157027, 'recall': 0.29642857142857143, 'f1': 0.31800766283524906, 'number': 280} | {'precision': 0.84, 'recall': 0.7241379310344828, 'f1': 0.7777777777777777, 'number': 87}              | {'precision': 0.5373134328358209, 'recall': 0.7058823529411765, 'f1': 0.6101694915254238, 'number': 255} | {'precision': 0.6927312775330396, 'recall': 0.7008356545961003, 'f1': 0.6967599003046248, 'number': 1795} | {'precision': 0.6132789749563191, 'recall': 0.6959682749504296, 'f1': 0.6520123839009287, 'number': 1513} | {'precision': 0.704323570432357, 'recall': 0.7266187050359713, 'f1': 0.7152974504249292, 'number': 1390}  | {'precision': 0.5159817351598174, 'recall': 0.598939929328622, 'f1': 0.55437448896157, 'number': 566}   | {'precision': 0.4633333333333333, 'recall': 0.5082266910420475, 'f1': 0.4847428073234525, 'number': 547}   | {'precision': 0.49206349206349204, 'recall': 0.5391304347826087, 'f1': 0.5145228215767634, 'number': 115} | 0.6280            | 0.6766         | 0.6514     | 0.9015           |
+| 0.2556        | 3.9983 | 4626 | 0.3324          | {'precision': 0.8854961832061069, 'recall': 0.8656716417910447, 'f1': 0.8754716981132075, 'number': 134} | {'precision': 0.6675774134790529, 'recall': 0.7426545086119554, 'f1': 0.7031175059952038, 'number': 987} | {'precision': 0.6914893617021277, 'recall': 0.7442748091603053, 'f1': 0.7169117647058824, 'number': 262} | {'precision': 0.3287671232876712, 'recall': 0.34285714285714286, 'f1': 0.3356643356643356, 'number': 280}   | {'precision': 0.7529411764705882, 'recall': 0.735632183908046, 'f1': 0.7441860465116279, 'number': 87} | {'precision': 0.5590062111801242, 'recall': 0.7058823529411765, 'f1': 0.6239168110918544, 'number': 255} | {'precision': 0.6794407042982911, 'recall': 0.7309192200557103, 'f1': 0.7042404723564144, 'number': 1795} | {'precision': 0.6267441860465116, 'recall': 0.712491738268341, 'f1': 0.6668728734921126, 'number': 1513}  | {'precision': 0.6789052069425902, 'recall': 0.7316546762589928, 'f1': 0.7042936288088643, 'number': 1390} | {'precision': 0.522273425499232, 'recall': 0.6007067137809188, 'f1': 0.5587510271158588, 'number': 566} | {'precision': 0.46021840873634945, 'recall': 0.5393053016453382, 'f1': 0.49663299663299665, 'number': 547} | {'precision': 0.49645390070921985, 'recall': 0.6086956521739131, 'f1': 0.5468749999999999, 'number': 115} | 0.6251            | 0.6930         | 0.6573     | 0.8992           |
 
 
 ### Framework versions