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 [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1391
-- Hard: {'precision': 0.6772616136919315, 'recall': 0.760989010989011, 'f1': 0.7166882276843466, 'number': 364}
-- Soft: {'precision': 0.6883116883116883, 'recall': 0.803030303030303, 'f1': 0.7412587412587411, 'number': 66}
-- Overall Precision: 0.6790
-- Overall Recall: 0.7674
-- Overall F1: 0.7205
-- Overall Accuracy: 0.9533
+- Loss: 0.1095
+- Hard: {'precision': 0.7445544554455445, 'recall': 0.8245614035087719, 'f1': 0.7825182101977106, 'number': 456}
+- Soft: {'precision': 0.7272727272727273, 'recall': 0.7804878048780488, 'f1': 0.7529411764705882, 'number': 82}
+- Overall Precision: 0.7420
+- Overall Recall: 0.8178
+- Overall F1: 0.7781
+- Overall Accuracy: 0.9635
 
 ## 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  | 0.1284          | {'precision': 0.5326315789473685, 'recall': 0.695054945054945, 'f1': 0.6030989272943981, 'number': 364}  | {'precision': 0.5, 'recall': 0.5757575757575758, 'f1': 0.5352112676056339, 'number': 66}                | 0.5281            | 0.6767         | 0.5933     | 0.9463           |
-| No log        | 2.0   | 356  | 0.1157          | {'precision': 0.6073059360730594, 'recall': 0.7307692307692307, 'f1': 0.6633416458852868, 'number': 364} | {'precision': 0.631578947368421, 'recall': 0.7272727272727273, 'f1': 0.676056338028169, 'number': 66}   | 0.6109            | 0.7302         | 0.6653     | 0.9519           |
-| 0.1468        | 3.0   | 534  | 0.1286          | {'precision': 0.6846153846153846, 'recall': 0.7335164835164835, 'f1': 0.7082228116710876, 'number': 364} | {'precision': 0.6582278481012658, 'recall': 0.7878787878787878, 'f1': 0.7172413793103448, 'number': 66} | 0.6802            | 0.7419         | 0.7097     | 0.9547           |
-| 0.1468        | 4.0   | 712  | 0.1383          | {'precision': 0.6799007444168734, 'recall': 0.7527472527472527, 'f1': 0.7144719687092568, 'number': 364} | {'precision': 0.6582278481012658, 'recall': 0.7878787878787878, 'f1': 0.7172413793103448, 'number': 66} | 0.6763            | 0.7581         | 0.7149     | 0.9544           |
-| 0.1468        | 5.0   | 890  | 0.1391          | {'precision': 0.6772616136919315, 'recall': 0.760989010989011, 'f1': 0.7166882276843466, 'number': 364}  | {'precision': 0.6883116883116883, 'recall': 0.803030303030303, 'f1': 0.7412587412587411, 'number': 66}  | 0.6790            | 0.7674         | 0.7205     | 0.9533           |
+| No log        | 1.0   | 178  | 0.1255          | {'precision': 0.583756345177665, 'recall': 0.756578947368421, 'f1': 0.659025787965616, 'number': 456}    | {'precision': 0.5425531914893617, 'recall': 0.6219512195121951, 'f1': 0.5795454545454546, 'number': 82} | 0.5781            | 0.7361         | 0.6476     | 0.9515           |
+| No log        | 2.0   | 356  | 0.1071          | {'precision': 0.6994219653179191, 'recall': 0.7960526315789473, 'f1': 0.7446153846153847, 'number': 456} | {'precision': 0.6129032258064516, 'recall': 0.6951219512195121, 'f1': 0.6514285714285714, 'number': 82} | 0.6863            | 0.7807         | 0.7304     | 0.9585           |
+| 0.1562        | 3.0   | 534  | 0.0990          | {'precision': 0.7150943396226415, 'recall': 0.831140350877193, 'f1': 0.7687626774847871, 'number': 456}  | {'precision': 0.6777777777777778, 'recall': 0.7439024390243902, 'f1': 0.7093023255813954, 'number': 82} | 0.7097            | 0.8178         | 0.7599     | 0.9621           |
+| 0.1562        | 4.0   | 712  | 0.1072          | {'precision': 0.7258687258687259, 'recall': 0.8245614035087719, 'f1': 0.7720739219712526, 'number': 456} | {'precision': 0.7222222222222222, 'recall': 0.7926829268292683, 'f1': 0.7558139534883721, 'number': 82} | 0.7253            | 0.8197         | 0.7696     | 0.9628           |
+| 0.1562        | 5.0   | 890  | 0.1095          | {'precision': 0.7445544554455445, 'recall': 0.8245614035087719, 'f1': 0.7825182101977106, 'number': 456} | {'precision': 0.7272727272727273, 'recall': 0.7804878048780488, 'f1': 0.7529411764705882, 'number': 82} | 0.7420            | 0.8178         | 0.7781     | 0.9635           |
 
 
 ### Framework versions