End of training

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: lilyyellow/my_awesome_ner-token_classification_v1.0.7-5
+base_model: NlpHUST/ner-vietnamese-electra-base
 tags:
 - generated_from_trainer
 model-index:
@@ -12,27 +12,27 @@ should probably proofread and complete it, then remove this comment. -->
 
 # my_awesome_ner-token_classification_v1.0.7-5
 
-This model is a fine-tuned version of [lilyyellow/my_awesome_ner-token_classification_v1.0.7-5](https://huggingface.co/lilyyellow/my_awesome_ner-token_classification_v1.0.7-5) on an unknown dataset.
+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: 0.5208
-- Age: {'precision': 0.8493150684931506, 'recall': 0.9393939393939394, 'f1': 0.8920863309352518, 'number': 132}
-- Datetime: {'precision': 0.7049180327868853, 'recall': 0.7428861788617886, 'f1': 0.723404255319149, 'number': 984}
-- Disease: {'precision': 0.6953405017921147, 'recall': 0.6855123674911661, 'f1': 0.6903914590747331, 'number': 283}
-- Event: {'precision': 0.30033003300330036, 'recall': 0.3446969696969697, 'f1': 0.3209876543209877, 'number': 264}
-- Gender: {'precision': 0.7647058823529411, 'recall': 0.7982456140350878, 'f1': 0.7811158798283262, 'number': 114}
-- Law: {'precision': 0.5303514376996805, 'recall': 0.6561264822134387, 'f1': 0.5865724381625441, 'number': 253}
-- Location: {'precision': 0.7111228255139694, 'recall': 0.7375615090213231, 'f1': 0.7241009125067096, 'number': 1829}
-- Organization: {'precision': 0.6420640104506858, 'recall': 0.6991465149359887, 'f1': 0.6693905345590739, 'number': 1406}
-- Person: {'precision': 0.6987087517934003, 'recall': 0.7295880149812735, 'f1': 0.7138145840967388, 'number': 1335}
-- Phone: {'precision': 0.8522727272727273, 'recall': 0.9615384615384616, 'f1': 0.9036144578313254, 'number': 78}
-- Product: {'precision': 0.4, 'recall': 0.3828125, 'f1': 0.3912175648702595, 'number': 256}
-- Quantity: {'precision': 0.5313001605136437, 'recall': 0.6084558823529411, 'f1': 0.567266495287061, 'number': 544}
-- Role: {'precision': 0.4302721088435374, 'recall': 0.48747591522157996, 'f1': 0.45709123757904246, 'number': 519}
-- Transportation: {'precision': 0.5, 'recall': 0.6231884057971014, 'f1': 0.5548387096774193, 'number': 138}
-- Overall Precision: 0.6349
-- Overall Recall: 0.6817
-- Overall F1: 0.6575
-- Overall Accuracy: 0.8878
+- Loss: 0.3789
+- Age: {'precision': 0.8503401360544217, 'recall': 0.946969696969697, 'f1': 0.8960573476702508, 'number': 132}
+- Datetime: {'precision': 0.6935483870967742, 'recall': 0.7428861788617886, 'f1': 0.7173699705593719, 'number': 984}
+- Disease: {'precision': 0.6895306859205776, 'recall': 0.6749116607773852, 'f1': 0.6821428571428573, 'number': 283}
+- Event: {'precision': 0.3210702341137124, 'recall': 0.36363636363636365, 'f1': 0.3410301953818828, 'number': 264}
+- Gender: {'precision': 0.7704918032786885, 'recall': 0.8245614035087719, 'f1': 0.7966101694915254, 'number': 114}
+- Law: {'precision': 0.5617283950617284, 'recall': 0.7193675889328063, 'f1': 0.6308492201039861, 'number': 253}
+- Location: {'precision': 0.6985105290190036, 'recall': 0.7435757244395844, 'f1': 0.7203389830508473, 'number': 1829}
+- Organization: {'precision': 0.640555906506633, 'recall': 0.7211948790896159, 'f1': 0.6784877885580463, 'number': 1406}
+- Person: {'precision': 0.7024147727272727, 'recall': 0.7408239700374532, 'f1': 0.7211082756106453, 'number': 1335}
+- Phone: {'precision': 0.8705882352941177, 'recall': 0.9487179487179487, 'f1': 0.9079754601226994, 'number': 78}
+- Product: {'precision': 0.3686274509803922, 'recall': 0.3671875, 'f1': 0.36790606653620356, 'number': 256}
+- Quantity: {'precision': 0.5566502463054187, 'recall': 0.6231617647058824, 'f1': 0.588031222896791, 'number': 544}
+- Role: {'precision': 0.4342560553633218, 'recall': 0.4836223506743738, 'f1': 0.45761166818596166, 'number': 519}
+- Transportation: {'precision': 0.49122807017543857, 'recall': 0.6086956521739131, 'f1': 0.5436893203883495, 'number': 138}
+- Overall Precision: 0.6348
+- Overall Recall: 0.6913
+- Overall F1: 0.6619
+- Overall Accuracy: 0.8912
 
 ## Model description
 
@@ -51,7 +51,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2e-05
+- learning_rate: 5e-05
 - train_batch_size: 16
 - eval_batch_size: 16
 - seed: 42
@@ -61,10 +61,10 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch  | Step | Validation Loss | Age                                                                                                      | Datetime                                                                                                 | Disease                                                                                                  | Event                                                                                                     | Gender                                                                                                   | Law                                                                                                      | Location                                                                                                  | Organization                                                                                              | Person                                                                                                    | Phone                                                                                                   | Product                                                                                            | Quantity                                                                                                 | Role                                                                                                       | Transportation                                                                                            | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.1172        | 1.9991 | 2313 | 0.4711          | {'precision': 0.8620689655172413, 'recall': 0.946969696969697, 'f1': 0.9025270758122743, 'number': 132}  | {'precision': 0.6928909952606636, 'recall': 0.7428861788617886, 'f1': 0.7170181461500736, 'number': 984} | {'precision': 0.7089552238805971, 'recall': 0.6713780918727915, 'f1': 0.689655172413793, 'number': 283}  | {'precision': 0.3090909090909091, 'recall': 0.32196969696969696, 'f1': 0.3153988868274582, 'number': 264} | {'precision': 0.7520661157024794, 'recall': 0.7982456140350878, 'f1': 0.7744680851063831, 'number': 114} | {'precision': 0.5795053003533569, 'recall': 0.6482213438735178, 'f1': 0.6119402985074627, 'number': 253} | {'precision': 0.7174721189591078, 'recall': 0.7386550027337343, 'f1': 0.7279094827586208, 'number': 1829} | {'precision': 0.6510554089709762, 'recall': 0.7019914651493598, 'f1': 0.675564681724846, 'number': 1406}  | {'precision': 0.720666161998486, 'recall': 0.7131086142322097, 'f1': 0.716867469879518, 'number': 1335}   | {'precision': 0.7816091954022989, 'recall': 0.8717948717948718, 'f1': 0.8242424242424243, 'number': 78} | {'precision': 0.38288288288288286, 'recall': 0.33203125, 'f1': 0.35564853556485354, 'number': 256} | {'precision': 0.5684575389948007, 'recall': 0.6029411764705882, 'f1': 0.5851917930419268, 'number': 544} | {'precision': 0.4645390070921986, 'recall': 0.5048169556840078, 'f1': 0.4838411819021238, 'number': 519}   | {'precision': 0.47368421052631576, 'recall': 0.5869565217391305, 'f1': 0.5242718446601942, 'number': 138} | 0.6480            | 0.6761         | 0.6617     | 0.8890           |
-| 0.0813        | 3.9983 | 4626 | 0.5208          | {'precision': 0.8493150684931506, 'recall': 0.9393939393939394, 'f1': 0.8920863309352518, 'number': 132} | {'precision': 0.7049180327868853, 'recall': 0.7428861788617886, 'f1': 0.723404255319149, 'number': 984}  | {'precision': 0.6953405017921147, 'recall': 0.6855123674911661, 'f1': 0.6903914590747331, 'number': 283} | {'precision': 0.30033003300330036, 'recall': 0.3446969696969697, 'f1': 0.3209876543209877, 'number': 264} | {'precision': 0.7647058823529411, 'recall': 0.7982456140350878, 'f1': 0.7811158798283262, 'number': 114} | {'precision': 0.5303514376996805, 'recall': 0.6561264822134387, 'f1': 0.5865724381625441, 'number': 253} | {'precision': 0.7111228255139694, 'recall': 0.7375615090213231, 'f1': 0.7241009125067096, 'number': 1829} | {'precision': 0.6420640104506858, 'recall': 0.6991465149359887, 'f1': 0.6693905345590739, 'number': 1406} | {'precision': 0.6987087517934003, 'recall': 0.7295880149812735, 'f1': 0.7138145840967388, 'number': 1335} | {'precision': 0.8522727272727273, 'recall': 0.9615384615384616, 'f1': 0.9036144578313254, 'number': 78} | {'precision': 0.4, 'recall': 0.3828125, 'f1': 0.3912175648702595, 'number': 256}                   | {'precision': 0.5313001605136437, 'recall': 0.6084558823529411, 'f1': 0.567266495287061, 'number': 544}  | {'precision': 0.4302721088435374, 'recall': 0.48747591522157996, 'f1': 0.45709123757904246, 'number': 519} | {'precision': 0.5, 'recall': 0.6231884057971014, 'f1': 0.5548387096774193, 'number': 138}                 | 0.6349            | 0.6817         | 0.6575     | 0.8878           |
+| Training Loss | Epoch  | Step | Validation Loss | Age                                                                                                     | Datetime                                                                                                 | Disease                                                                                                  | Event                                                                                                      | Gender                                                                                                   | Law                                                                                                      | Location                                                                                                  | Organization                                                                                              | Person                                                                                                    | Phone                                                                                                   | Product                                                                                          | Quantity                                                                                                | Role                                                                                                      | Transportation                                                                                            | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.29          | 1.9991 | 2313 | 0.3353          | {'precision': 0.8561643835616438, 'recall': 0.946969696969697, 'f1': 0.8992805755395684, 'number': 132} | {'precision': 0.707647628267183, 'recall': 0.7428861788617886, 'f1': 0.7248388696083291, 'number': 984}  | {'precision': 0.6946564885496184, 'recall': 0.6431095406360424, 'f1': 0.6678899082568808, 'number': 283} | {'precision': 0.34191176470588236, 'recall': 0.3522727272727273, 'f1': 0.34701492537313433, 'number': 264} | {'precision': 0.7560975609756098, 'recall': 0.8157894736842105, 'f1': 0.7848101265822786, 'number': 114} | {'precision': 0.5384615384615384, 'recall': 0.6363636363636364, 'f1': 0.5833333333333334, 'number': 253} | {'precision': 0.7157279489904357, 'recall': 0.7364680153089119, 'f1': 0.7259498787388844, 'number': 1829} | {'precision': 0.6326268464996788, 'recall': 0.7005689900426743, 'f1': 0.6648666891663854, 'number': 1406} | {'precision': 0.7298136645962733, 'recall': 0.704119850187266, 'f1': 0.7167365611894777, 'number': 1335}  | {'precision': 0.8072289156626506, 'recall': 0.8589743589743589, 'f1': 0.8322981366459627, 'number': 78} | {'precision': 0.425, 'recall': 0.265625, 'f1': 0.32692307692307687, 'number': 256}               | {'precision': 0.5797101449275363, 'recall': 0.5882352941176471, 'f1': 0.583941605839416, 'number': 544} | {'precision': 0.4549019607843137, 'recall': 0.44701348747591524, 'f1': 0.4509232264334305, 'number': 519} | {'precision': 0.5194805194805194, 'recall': 0.5797101449275363, 'f1': 0.5479452054794519, 'number': 138}  | 0.6518            | 0.6667         | 0.6592     | 0.8937           |
+| 0.1806        | 3.9983 | 4626 | 0.3789          | {'precision': 0.8503401360544217, 'recall': 0.946969696969697, 'f1': 0.8960573476702508, 'number': 132} | {'precision': 0.6935483870967742, 'recall': 0.7428861788617886, 'f1': 0.7173699705593719, 'number': 984} | {'precision': 0.6895306859205776, 'recall': 0.6749116607773852, 'f1': 0.6821428571428573, 'number': 283} | {'precision': 0.3210702341137124, 'recall': 0.36363636363636365, 'f1': 0.3410301953818828, 'number': 264}  | {'precision': 0.7704918032786885, 'recall': 0.8245614035087719, 'f1': 0.7966101694915254, 'number': 114} | {'precision': 0.5617283950617284, 'recall': 0.7193675889328063, 'f1': 0.6308492201039861, 'number': 253} | {'precision': 0.6985105290190036, 'recall': 0.7435757244395844, 'f1': 0.7203389830508473, 'number': 1829} | {'precision': 0.640555906506633, 'recall': 0.7211948790896159, 'f1': 0.6784877885580463, 'number': 1406}  | {'precision': 0.7024147727272727, 'recall': 0.7408239700374532, 'f1': 0.7211082756106453, 'number': 1335} | {'precision': 0.8705882352941177, 'recall': 0.9487179487179487, 'f1': 0.9079754601226994, 'number': 78} | {'precision': 0.3686274509803922, 'recall': 0.3671875, 'f1': 0.36790606653620356, 'number': 256} | {'precision': 0.5566502463054187, 'recall': 0.6231617647058824, 'f1': 0.588031222896791, 'number': 544} | {'precision': 0.4342560553633218, 'recall': 0.4836223506743738, 'f1': 0.45761166818596166, 'number': 519} | {'precision': 0.49122807017543857, 'recall': 0.6086956521739131, 'f1': 0.5436893203883495, 'number': 138} | 0.6348            | 0.6913         | 0.6619     | 0.8912           |
 
 
 ### Framework versions

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "lilyyellow/my_awesome_ner-token_classification_v1.0.7-5",
+  "_name_or_path": "NlpHUST/ner-vietnamese-electra-base",
   "architectures": [
     "ElectraForTokenClassification"
   ],

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:046ab598432a33d2f26909ce96bd3c10503cdaed64e54542daf215bb0dde4bbb
 size 532380148

training_args.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ba4cc3b9994e50a8607f06cd4430123c5d7dd6857e10a067b7ba55a230862558
+oid sha256:1e42d71195aa8767dfda61434e3be1ee96a57619534f34beecd9af0314b08a4c
 size 5112