End of training

README.md

@@ -15,13 +15,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [dathi103/gbert-job-extended](https://huggingface.co/dathi103/gbert-job-extended) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1217
-- Hard: {'precision': 0.7340153452685422, 'recall': 0.790633608815427, 'f1': 0.7612732095490715, 'number': 363}
-- Soft: {'precision': 0.6911764705882353, 'recall': 0.7121212121212122, 'f1': 0.7014925373134329, 'number': 66}
-- Overall Precision: 0.7277
-- Overall Recall: 0.7786
-- Overall F1: 0.7523
-- Overall Accuracy: 0.9661
+- Loss: 0.0806
+- Hard: {'precision': 0.7995867768595041, 'recall': 0.8524229074889867, 'f1': 0.8251599147121534, 'number': 454}
+- Soft: {'precision': 0.7804878048780488, 'recall': 0.7804878048780488, 'f1': 0.7804878048780488, 'number': 82}
+- Overall Precision: 0.7968
+- Overall Recall: 0.8414
+- Overall F1: 0.8185
+- Overall Accuracy: 0.9750
 
 ## Model description
 
@@ -52,11 +52,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Hard                                                                                                     | Soft                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| No log        | 1.0   | 178  | 0.1108          | {'precision': 0.6256038647342995, 'recall': 0.7134986225895317, 'f1': 0.6666666666666667, 'number': 363} | {'precision': 0.5606060606060606, 'recall': 0.5606060606060606, 'f1': 0.5606060606060606, 'number': 66} | 0.6167            | 0.6900         | 0.6513     | 0.9593           |
-| No log        | 2.0   | 356  | 0.1027          | {'precision': 0.6860759493670886, 'recall': 0.7465564738292011, 'f1': 0.7150395778364115, 'number': 363} | {'precision': 0.7096774193548387, 'recall': 0.6666666666666666, 'f1': 0.6875, 'number': 66}             | 0.6893            | 0.7343         | 0.7111     | 0.9639           |
-| 0.1153        | 3.0   | 534  | 0.1085          | {'precision': 0.7085427135678392, 'recall': 0.7768595041322314, 'f1': 0.7411300919842312, 'number': 363} | {'precision': 0.6533333333333333, 'recall': 0.7424242424242424, 'f1': 0.6950354609929078, 'number': 66} | 0.6998            | 0.7716         | 0.7339     | 0.9658           |
-| 0.1153        | 4.0   | 712  | 0.1163          | {'precision': 0.6987341772151898, 'recall': 0.7603305785123967, 'f1': 0.7282321899736148, 'number': 363} | {'precision': 0.7121212121212122, 'recall': 0.7121212121212122, 'f1': 0.7121212121212122, 'number': 66} | 0.7007            | 0.7529         | 0.7258     | 0.9657           |
-| 0.1153        | 5.0   | 890  | 0.1217          | {'precision': 0.7340153452685422, 'recall': 0.790633608815427, 'f1': 0.7612732095490715, 'number': 363}  | {'precision': 0.6911764705882353, 'recall': 0.7121212121212122, 'f1': 0.7014925373134329, 'number': 66} | 0.7277            | 0.7786         | 0.7523     | 0.9661           |
+| No log        | 1.0   | 178  | 0.1035          | {'precision': 0.6715867158671587, 'recall': 0.801762114537445, 'f1': 0.7309236947791165, 'number': 454}  | {'precision': 0.6105263157894737, 'recall': 0.7073170731707317, 'f1': 0.6553672316384181, 'number': 82} | 0.6625            | 0.7873         | 0.7195     | 0.9592           |
+| No log        | 2.0   | 356  | 0.0762          | {'precision': 0.7698744769874477, 'recall': 0.8105726872246696, 'f1': 0.7896995708154506, 'number': 454} | {'precision': 0.7532467532467533, 'recall': 0.7073170731707317, 'f1': 0.7295597484276729, 'number': 82} | 0.7676            | 0.7948         | 0.7809     | 0.9705           |
+| 0.1183        | 3.0   | 534  | 0.0713          | {'precision': 0.7958762886597938, 'recall': 0.8502202643171806, 'f1': 0.8221512247071352, 'number': 454} | {'precision': 0.7974683544303798, 'recall': 0.7682926829268293, 'f1': 0.782608695652174, 'number': 82}  | 0.7961            | 0.8377         | 0.8164     | 0.9735           |
+| 0.1183        | 4.0   | 712  | 0.0785          | {'precision': 0.7962962962962963, 'recall': 0.8524229074889867, 'f1': 0.823404255319149, 'number': 454}  | {'precision': 0.7901234567901234, 'recall': 0.7804878048780488, 'f1': 0.7852760736196319, 'number': 82} | 0.7954            | 0.8414         | 0.8178     | 0.9739           |
+| 0.1183        | 5.0   | 890  | 0.0806          | {'precision': 0.7995867768595041, 'recall': 0.8524229074889867, 'f1': 0.8251599147121534, 'number': 454} | {'precision': 0.7804878048780488, 'recall': 0.7804878048780488, 'f1': 0.7804878048780488, 'number': 82} | 0.7968            | 0.8414         | 0.8185     | 0.9750           |
 
 
 ### Framework versions