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-extended](https://huggingface.co/dathi103/bert-job-german-extended) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1171
-- Hard: {'precision': 0.7376681614349776, 'recall': 0.8225, 'f1': 0.7777777777777777, 'number': 800}
-- Soft: {'precision': 0.7541899441340782, 'recall': 0.8709677419354839, 'f1': 0.8083832335329341, 'number': 155}
-- Overall Precision: 0.7404
-- Overall Recall: 0.8304
-- Overall F1: 0.7828
-- Overall Accuracy: 0.9675
+- Loss: 0.1440
+- Hard: {'precision': 0.7093596059113301, 'recall': 0.7933884297520661, 'f1': 0.7490247074122237, 'number': 363}
+- Soft: {'precision': 0.7058823529411765, 'recall': 0.7272727272727273, 'f1': 0.7164179104477613, 'number': 66}
+- Overall Precision: 0.7089
+- Overall Recall: 0.7832
+- Overall F1: 0.7442
+- Overall Accuracy: 0.9650
 
 ## Model description
 
@@ -50,13 +50,13 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Hard                                                                                         | Soft                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| No log        | 1.0   | 158  | 0.1042          | {'precision': 0.6269592476489029, 'recall': 0.75, 'f1': 0.6829823562891292, 'number': 800}   | {'precision': 0.6632124352331606, 'recall': 0.8258064516129032, 'f1': 0.735632183908046, 'number': 155}  | 0.6330            | 0.7623         | 0.6917     | 0.9604           |
-| No log        | 2.0   | 316  | 0.0984          | {'precision': 0.6931567328918322, 'recall': 0.785, 'f1': 0.7362250879249707, 'number': 800}  | {'precision': 0.6084905660377359, 'recall': 0.832258064516129, 'f1': 0.7029972752043598, 'number': 155}  | 0.6771            | 0.7927         | 0.7303     | 0.9635           |
-| No log        | 3.0   | 474  | 0.1017          | {'precision': 0.7263513513513513, 'recall': 0.80625, 'f1': 0.764218009478673, 'number': 800} | {'precision': 0.696969696969697, 'recall': 0.8903225806451613, 'f1': 0.7818696883852692, 'number': 155}  | 0.7210            | 0.8199         | 0.7673     | 0.9674           |
-| 0.1083        | 4.0   | 632  | 0.1105          | {'precision': 0.7414187643020596, 'recall': 0.81, 'f1': 0.7741935483870969, 'number': 800}   | {'precision': 0.7653631284916201, 'recall': 0.8838709677419355, 'f1': 0.8203592814371258, 'number': 155} | 0.7455            | 0.8220         | 0.7819     | 0.9685           |
-| 0.1083        | 5.0   | 790  | 0.1171          | {'precision': 0.7376681614349776, 'recall': 0.8225, 'f1': 0.7777777777777777, 'number': 800} | {'precision': 0.7541899441340782, 'recall': 0.8709677419354839, 'f1': 0.8083832335329341, 'number': 155} | 0.7404            | 0.8304         | 0.7828     | 0.9675           |
+| Training Loss | Epoch | Step | Validation Loss | Hard                                                                                                     | Soft                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| No log        | 1.0   | 178  | 0.1035          | {'precision': 0.6582278481012658, 'recall': 0.7162534435261708, 'f1': 0.6860158311345645, 'number': 363} | {'precision': 0.6451612903225806, 'recall': 0.6060606060606061, 'f1': 0.625, 'number': 66}              | 0.6565            | 0.6993         | 0.6772     | 0.9597           |
+| No log        | 2.0   | 356  | 0.1067          | {'precision': 0.6641414141414141, 'recall': 0.7245179063360881, 'f1': 0.6930171277997365, 'number': 363} | {'precision': 0.676923076923077, 'recall': 0.6666666666666666, 'f1': 0.6717557251908397, 'number': 66}  | 0.6659            | 0.7156         | 0.6899     | 0.9634           |
+| 0.1072        | 3.0   | 534  | 0.1204          | {'precision': 0.7079207920792079, 'recall': 0.7878787878787878, 'f1': 0.7457627118644068, 'number': 363} | {'precision': 0.6956521739130435, 'recall': 0.7272727272727273, 'f1': 0.711111111111111, 'number': 66}  | 0.7061            | 0.7786         | 0.7406     | 0.9652           |
+| 0.1072        | 4.0   | 712  | 0.1350          | {'precision': 0.7178841309823678, 'recall': 0.7851239669421488, 'f1': 0.7500000000000001, 'number': 363} | {'precision': 0.6956521739130435, 'recall': 0.7272727272727273, 'f1': 0.711111111111111, 'number': 66}  | 0.7146            | 0.7762         | 0.7441     | 0.9644           |
+| 0.1072        | 5.0   | 890  | 0.1440          | {'precision': 0.7093596059113301, 'recall': 0.7933884297520661, 'f1': 0.7490247074122237, 'number': 363} | {'precision': 0.7058823529411765, 'recall': 0.7272727272727273, 'f1': 0.7164179104477613, 'number': 66} | 0.7089            | 0.7832         | 0.7442     | 0.9650           |
 
 
 ### Framework versions