End of training

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: NlpHUST/ner-vietnamese-electra-base
+base_model: lilyyellow/my_awesome_ner-token_classification_v1.0.7-6
 tags:
 - generated_from_trainer
 model-index:
@@ -12,25 +12,25 @@ should probably proofread and complete it, then remove this comment. -->
 
 # my_awesome_ner-token_classification_v1.0.7-6
 
-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.
+This model is a fine-tuned version of [lilyyellow/my_awesome_ner-token_classification_v1.0.7-6](https://huggingface.co/lilyyellow/my_awesome_ner-token_classification_v1.0.7-6) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3274
-- Age: {'precision': 0.9069767441860465, 'recall': 0.8731343283582089, 'f1': 0.8897338403041826, 'number': 134}
-- Datetime: {'precision': 0.6740947075208914, 'recall': 0.7355623100303952, 'f1': 0.7034883720930233, 'number': 987}
-- Disease: {'precision': 0.6631944444444444, 'recall': 0.7290076335877863, 'f1': 0.6945454545454546, 'number': 262}
-- Event: {'precision': 0.290625, 'recall': 0.33214285714285713, 'f1': 0.31, 'number': 280}
-- Gender: {'precision': 0.8266666666666667, 'recall': 0.7126436781609196, 'f1': 0.7654320987654321, 'number': 87}
-- Law: {'precision': 0.5854430379746836, 'recall': 0.7254901960784313, 'f1': 0.647985989492119, 'number': 255}
-- Location: {'precision': 0.6700662927078022, 'recall': 0.732033426183844, 'f1': 0.6996805111821087, 'number': 1795}
-- Organization: {'precision': 0.5947934352009054, 'recall': 0.6946463978849967, 'f1': 0.6408536585365853, 'number': 1513}
-- Person: {'precision': 0.6908841672378341, 'recall': 0.7251798561151079, 'f1': 0.7076167076167076, 'number': 1390}
-- Quantity: {'precision': 0.5075528700906344, 'recall': 0.5936395759717314, 'f1': 0.5472312703583061, 'number': 566}
-- Role: {'precision': 0.465818759936407, 'recall': 0.5356489945155393, 'f1': 0.49829931972789115, 'number': 547}
-- Transportation: {'precision': 0.46153846153846156, 'recall': 0.5217391304347826, 'f1': 0.4897959183673469, 'number': 115}
-- Overall Precision: 0.6168
-- Overall Recall: 0.6854
-- Overall F1: 0.6493
-- Overall Accuracy: 0.8998
+- Loss: 0.3464
+- Age: {'precision': 0.9153846153846154, 'recall': 0.8880597014925373, 'f1': 0.9015151515151514, 'number': 134}
+- Datetime: {'precision': 0.6675824175824175, 'recall': 0.7386018237082067, 'f1': 0.7012987012987013, 'number': 987}
+- Disease: {'precision': 0.6712328767123288, 'recall': 0.7480916030534351, 'f1': 0.7075812274368231, 'number': 262}
+- Event: {'precision': 0.28023598820059, 'recall': 0.3392857142857143, 'f1': 0.30694668820678517, 'number': 280}
+- Gender: {'precision': 0.6947368421052632, 'recall': 0.7586206896551724, 'f1': 0.7252747252747253, 'number': 87}
+- Law: {'precision': 0.5664556962025317, 'recall': 0.7019607843137254, 'f1': 0.626970227670753, 'number': 255}
+- Location: {'precision': 0.6860587002096437, 'recall': 0.7292479108635097, 'f1': 0.7069943289224953, 'number': 1795}
+- Organization: {'precision': 0.6145833333333334, 'recall': 0.7019167217448777, 'f1': 0.6553532860228325, 'number': 1513}
+- Person: {'precision': 0.6846725185685347, 'recall': 0.7294964028776978, 'f1': 0.7063740856844305, 'number': 1390}
+- Quantity: {'precision': 0.5110782865583456, 'recall': 0.6113074204946997, 'f1': 0.5567176186645212, 'number': 566}
+- Role: {'precision': 0.4672897196261682, 'recall': 0.5484460694698354, 'f1': 0.5046257359125315, 'number': 547}
+- Transportation: {'precision': 0.46, 'recall': 0.6, 'f1': 0.5207547169811321, 'number': 115}
+- Overall Precision: 0.6197
+- Overall Recall: 0.6915
+- Overall F1: 0.6536
+- Overall Accuracy: 0.8962
 
 ## Model description
 
@@ -59,9 +59,9 @@ 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 |
-|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.2854        | 1.9965 | 1156 | 0.3274          | {'precision': 0.9069767441860465, 'recall': 0.8731343283582089, 'f1': 0.8897338403041826, 'number': 134} | {'precision': 0.6740947075208914, 'recall': 0.7355623100303952, 'f1': 0.7034883720930233, 'number': 987} | {'precision': 0.6631944444444444, 'recall': 0.7290076335877863, 'f1': 0.6945454545454546, 'number': 262} | {'precision': 0.290625, 'recall': 0.33214285714285713, 'f1': 0.31, 'number': 280} | {'precision': 0.8266666666666667, 'recall': 0.7126436781609196, 'f1': 0.7654320987654321, 'number': 87} | {'precision': 0.5854430379746836, 'recall': 0.7254901960784313, 'f1': 0.647985989492119, 'number': 255} | {'precision': 0.6700662927078022, 'recall': 0.732033426183844, 'f1': 0.6996805111821087, 'number': 1795} | {'precision': 0.5947934352009054, 'recall': 0.6946463978849967, 'f1': 0.6408536585365853, 'number': 1513} | {'precision': 0.6908841672378341, 'recall': 0.7251798561151079, 'f1': 0.7076167076167076, 'number': 1390} | {'precision': 0.5075528700906344, 'recall': 0.5936395759717314, 'f1': 0.5472312703583061, 'number': 566} | {'precision': 0.465818759936407, 'recall': 0.5356489945155393, 'f1': 0.49829931972789115, 'number': 547} | {'precision': 0.46153846153846156, 'recall': 0.5217391304347826, 'f1': 0.4897959183673469, 'number': 115} | 0.6168            | 0.6854         | 0.6493     | 0.8998           |
+| 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.2089        | 1.9965 | 1156 | 0.3464          | {'precision': 0.9153846153846154, 'recall': 0.8880597014925373, 'f1': 0.9015151515151514, 'number': 134} | {'precision': 0.6675824175824175, 'recall': 0.7386018237082067, 'f1': 0.7012987012987013, 'number': 987} | {'precision': 0.6712328767123288, 'recall': 0.7480916030534351, 'f1': 0.7075812274368231, 'number': 262} | {'precision': 0.28023598820059, 'recall': 0.3392857142857143, 'f1': 0.30694668820678517, 'number': 280} | {'precision': 0.6947368421052632, 'recall': 0.7586206896551724, 'f1': 0.7252747252747253, 'number': 87} | {'precision': 0.5664556962025317, 'recall': 0.7019607843137254, 'f1': 0.626970227670753, 'number': 255} | {'precision': 0.6860587002096437, 'recall': 0.7292479108635097, 'f1': 0.7069943289224953, 'number': 1795} | {'precision': 0.6145833333333334, 'recall': 0.7019167217448777, 'f1': 0.6553532860228325, 'number': 1513} | {'precision': 0.6846725185685347, 'recall': 0.7294964028776978, 'f1': 0.7063740856844305, 'number': 1390} | {'precision': 0.5110782865583456, 'recall': 0.6113074204946997, 'f1': 0.5567176186645212, 'number': 566} | {'precision': 0.4672897196261682, 'recall': 0.5484460694698354, 'f1': 0.5046257359125315, 'number': 547} | {'precision': 0.46, 'recall': 0.6, 'f1': 0.5207547169811321, 'number': 115} | 0.6197            | 0.6915         | 0.6536     | 0.8962           |
 
 
 ### Framework versions

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "NlpHUST/ner-vietnamese-electra-base",
+  "_name_or_path": "lilyyellow/my_awesome_ner-token_classification_v1.0.7-6",
   "architectures": [
     "ElectraForTokenClassification"
   ],

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:17fbf0a9127acb66fcedd491aa15fd27b9bfae3b0a7575155c4c9b56bd6e99f9
 size 532367844

training_args.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ae934c411f42ed098a94aabeb09d3540809d82ecba967735ddf10cefe14a36d7
+oid sha256:319134ae4ead5a28da86f3be011f29b7b54f22de1a12863dec3a94e2a2a3e71f
 size 5112