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: 0.1070
-- Hard: {'precision': 0.7168742921857305, 'recall': 0.79125, 'f1': 0.7522281639928698, 'number': 800}
-- Soft: {'precision': 0.7176470588235294, 'recall': 0.7870967741935484, 'f1': 0.7507692307692306, 'number': 155}
-- Overall Precision: 0.7170
-- Overall Recall: 0.7906
-- Overall F1: 0.7520
-- Overall Accuracy: 0.9641
+- 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
 
 ## 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  | 0.1125          | {'precision': 0.5969387755102041, 'recall': 0.73125, 'f1': 0.6573033707865169, 'number': 800} | {'precision': 0.6706586826347305, 'recall': 0.7225806451612903, 'f1': 0.6956521739130435, 'number': 155} | 0.6077            | 0.7298         | 0.6632     | 0.9560           |
-| No log        | 2.0   | 316  | 0.1005          | {'precision': 0.6616862326574173, 'recall': 0.775, 'f1': 0.7138744962579161, 'number': 800}   | {'precision': 0.6344086021505376, 'recall': 0.7612903225806451, 'f1': 0.6920821114369502, 'number': 155} | 0.6572            | 0.7728         | 0.7103     | 0.9605           |
-| No log        | 3.0   | 474  | 0.0991          | {'precision': 0.7067415730337079, 'recall': 0.78625, 'f1': 0.744378698224852, 'number': 800}  | {'precision': 0.625, 'recall': 0.7741935483870968, 'f1': 0.69164265129683, 'number': 155}                | 0.6922            | 0.7843         | 0.7354     | 0.9645           |
-| 0.1191        | 4.0   | 632  | 0.1045          | {'precision': 0.7178329571106095, 'recall': 0.795, 'f1': 0.7544483985765125, 'number': 800}   | {'precision': 0.7245508982035929, 'recall': 0.7806451612903226, 'f1': 0.751552795031056, 'number': 155}  | 0.7189            | 0.7927         | 0.7540     | 0.9661           |
-| 0.1191        | 5.0   | 790  | 0.1070          | {'precision': 0.7168742921857305, 'recall': 0.79125, 'f1': 0.7522281639928698, 'number': 800} | {'precision': 0.7176470588235294, 'recall': 0.7870967741935484, 'f1': 0.7507692307692306, 'number': 155} | 0.7170            | 0.7906         | 0.7520     | 0.9641           |
+| 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           |
 
 
 ### Framework versions