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](https://huggingface.co/dathi103/gbert-job) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1383
-- Hard: {'precision': 0.6893203883495146, 'recall': 0.7802197802197802, 'f1': 0.7319587628865979, 'number': 364}
-- Soft: {'precision': 0.6329113924050633, 'recall': 0.7575757575757576, 'f1': 0.6896551724137931, 'number': 66}
-- Overall Precision: 0.6802
-- Overall Recall: 0.7767
-- Overall F1: 0.7253
-- Overall Accuracy: 0.9570
+- Loss: 0.1135
+- Hard: {'precision': 0.7519685039370079, 'recall': 0.8377192982456141, 'f1': 0.7925311203319502, 'number': 456}
+- Soft: {'precision': 0.6739130434782609, 'recall': 0.7560975609756098, 'f1': 0.7126436781609194, 'number': 82}
+- Overall Precision: 0.74
+- Overall Recall: 0.8253
+- Overall F1: 0.7803
+- Overall Accuracy: 0.9647
 
 ## 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  | 0.1186          | {'precision': 0.5478260869565217, 'recall': 0.6923076923076923, 'f1': 0.6116504854368932, 'number': 364} | {'precision': 0.5070422535211268, 'recall': 0.5454545454545454, 'f1': 0.5255474452554744, 'number': 66} | 0.5424            | 0.6698         | 0.5994     | 0.9511           |
-| No log        | 2.0   | 356  | 0.1073          | {'precision': 0.6247030878859857, 'recall': 0.7225274725274725, 'f1': 0.6700636942675159, 'number': 364} | {'precision': 0.573170731707317, 'recall': 0.7121212121212122, 'f1': 0.6351351351351352, 'number': 66}  | 0.6163            | 0.7209         | 0.6645     | 0.9572           |
-| 0.1381        | 3.0   | 534  | 0.1310          | {'precision': 0.6836734693877551, 'recall': 0.7362637362637363, 'f1': 0.7089947089947091, 'number': 364} | {'precision': 0.5632183908045977, 'recall': 0.7424242424242424, 'f1': 0.6405228758169934, 'number': 66} | 0.6618            | 0.7372         | 0.6975     | 0.9574           |
-| 0.1381        | 4.0   | 712  | 0.1350          | {'precision': 0.7019704433497537, 'recall': 0.782967032967033, 'f1': 0.7402597402597403, 'number': 364}  | {'precision': 0.6024096385542169, 'recall': 0.7575757575757576, 'f1': 0.6711409395973154, 'number': 66} | 0.6851            | 0.7791         | 0.7291     | 0.9564           |
-| 0.1381        | 5.0   | 890  | 0.1383          | {'precision': 0.6893203883495146, 'recall': 0.7802197802197802, 'f1': 0.7319587628865979, 'number': 364} | {'precision': 0.6329113924050633, 'recall': 0.7575757575757576, 'f1': 0.6896551724137931, 'number': 66} | 0.6802            | 0.7767         | 0.7253     | 0.9570           |
+| No log        | 1.0   | 178  | 0.1201          | {'precision': 0.6016949152542372, 'recall': 0.7785087719298246, 'f1': 0.678776290630975, 'number': 456}  | {'precision': 0.5894736842105263, 'recall': 0.6829268292682927, 'f1': 0.632768361581921, 'number': 82}  | 0.6               | 0.7639         | 0.6721     | 0.9508           |
+| No log        | 2.0   | 356  | 0.1010          | {'precision': 0.6853281853281853, 'recall': 0.7785087719298246, 'f1': 0.728952772073922, 'number': 456}  | {'precision': 0.632183908045977, 'recall': 0.6707317073170732, 'f1': 0.6508875739644969, 'number': 82}  | 0.6777            | 0.7621         | 0.7174     | 0.9603           |
+| 0.1417        | 3.0   | 534  | 0.1026          | {'precision': 0.7030075187969925, 'recall': 0.8201754385964912, 'f1': 0.757085020242915, 'number': 456}  | {'precision': 0.65625, 'recall': 0.7682926829268293, 'f1': 0.7078651685393258, 'number': 82}            | 0.6959            | 0.8123         | 0.7496     | 0.9598           |
+| 0.1417        | 4.0   | 712  | 0.1122          | {'precision': 0.7311411992263056, 'recall': 0.8289473684210527, 'f1': 0.776978417266187, 'number': 456}  | {'precision': 0.6464646464646465, 'recall': 0.7804878048780488, 'f1': 0.7071823204419891, 'number': 82} | 0.7175            | 0.8216         | 0.7660     | 0.9616           |
+| 0.1417        | 5.0   | 890  | 0.1135          | {'precision': 0.7519685039370079, 'recall': 0.8377192982456141, 'f1': 0.7925311203319502, 'number': 456} | {'precision': 0.6739130434782609, 'recall': 0.7560975609756098, 'f1': 0.7126436781609194, 'number': 82} | 0.74              | 0.8253         | 0.7803     | 0.9647           |
 
 
 ### Framework versions