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/bert-job-german](https://huggingface.co/dathi103/bert-job-german) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1565
-- Hard: {'precision': 0.683698296836983, 'recall': 0.771978021978022, 'f1': 0.7251612903225807, 'number': 364}
-- Soft: {'precision': 0.68, 'recall': 0.7727272727272727, 'f1': 0.7234042553191491, 'number': 66}
-- Overall Precision: 0.6831
-- Overall Recall: 0.7721
-- Overall F1: 0.7249
-- Overall Accuracy: 0.9584
+- Loss: 0.1171
+- Hard: {'precision': 0.7529644268774703, 'recall': 0.8355263157894737, 'f1': 0.7920997920997921, 'number': 456}
+- Soft: {'precision': 0.7906976744186046, 'recall': 0.8292682926829268, 'f1': 0.8095238095238095, 'number': 82}
+- Overall Precision: 0.7584
+- Overall Recall: 0.8346
+- Overall F1: 0.7947
+- Overall Accuracy: 0.9675
 
 ## 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  | 0.1225          | {'precision': 0.5902439024390244, 'recall': 0.6648351648351648, 'f1': 0.6253229974160206, 'number': 364} | {'precision': 0.625, 'recall': 0.6060606060606061, 'f1': 0.6153846153846154, 'number': 66}              | 0.5949            | 0.6558         | 0.6239     | 0.9537           |
-| No log        | 2.0   | 356  | 0.1176          | {'precision': 0.6282973621103117, 'recall': 0.7197802197802198, 'f1': 0.6709346991037132, 'number': 364} | {'precision': 0.6351351351351351, 'recall': 0.7121212121212122, 'f1': 0.6714285714285715, 'number': 66} | 0.6293            | 0.7186         | 0.6710     | 0.9563           |
-| 0.1349        | 3.0   | 534  | 0.1361          | {'precision': 0.6747572815533981, 'recall': 0.7637362637362637, 'f1': 0.7164948453608249, 'number': 364} | {'precision': 0.620253164556962, 'recall': 0.7424242424242424, 'f1': 0.6758620689655171, 'number': 66}  | 0.6660            | 0.7605         | 0.7101     | 0.9591           |
-| 0.1349        | 4.0   | 712  | 0.1499          | {'precision': 0.672289156626506, 'recall': 0.7664835164835165, 'f1': 0.7163029525032093, 'number': 364}  | {'precision': 0.6944444444444444, 'recall': 0.7575757575757576, 'f1': 0.7246376811594203, 'number': 66} | 0.6756            | 0.7651         | 0.7176     | 0.9587           |
-| 0.1349        | 5.0   | 890  | 0.1565          | {'precision': 0.683698296836983, 'recall': 0.771978021978022, 'f1': 0.7251612903225807, 'number': 364}   | {'precision': 0.68, 'recall': 0.7727272727272727, 'f1': 0.7234042553191491, 'number': 66}               | 0.6831            | 0.7721         | 0.7249     | 0.9584           |
+| No log        | 1.0   | 178  | 0.1177          | {'precision': 0.6027397260273972, 'recall': 0.7719298245614035, 'f1': 0.676923076923077, 'number': 456}  | {'precision': 0.6629213483146067, 'recall': 0.7195121951219512, 'f1': 0.6900584795321638, 'number': 82} | 0.6107            | 0.7639         | 0.6788     | 0.9524           |
+| No log        | 2.0   | 356  | 0.0978          | {'precision': 0.7474541751527495, 'recall': 0.8048245614035088, 'f1': 0.775079197465681, 'number': 456}  | {'precision': 0.7058823529411765, 'recall': 0.7317073170731707, 'f1': 0.718562874251497, 'number': 82}  | 0.7413            | 0.7937         | 0.7666     | 0.9620           |
+| 0.1344        | 3.0   | 534  | 0.1022          | {'precision': 0.7242718446601941, 'recall': 0.8179824561403509, 'f1': 0.768280123583934, 'number': 456}  | {'precision': 0.735632183908046, 'recall': 0.7804878048780488, 'f1': 0.757396449704142, 'number': 82}   | 0.7259            | 0.8123         | 0.7667     | 0.9632           |
+| 0.1344        | 4.0   | 712  | 0.1133          | {'precision': 0.762278978388998, 'recall': 0.8508771929824561, 'f1': 0.8041450777202073, 'number': 456}  | {'precision': 0.7555555555555555, 'recall': 0.8292682926829268, 'f1': 0.7906976744186047, 'number': 82} | 0.7613            | 0.8476         | 0.8021     | 0.9665           |
+| 0.1344        | 5.0   | 890  | 0.1171          | {'precision': 0.7529644268774703, 'recall': 0.8355263157894737, 'f1': 0.7920997920997921, 'number': 456} | {'precision': 0.7906976744186046, 'recall': 0.8292682926829268, 'f1': 0.8095238095238095, 'number': 82} | 0.7584            | 0.8346         | 0.7947     | 0.9675           |
 
 
 ### Framework versions