Update README.md

README.md

@@ -24,14 +24,31 @@ This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/
 
 It achieves the following results on the evaluation set:
 - Loss: 0.0949
-- Loc: {'precision': 0.9289891395154553, 'recall': 0.9336691855583543, 'f1': 0.931323283082077, 'number': 5955}
-- Misc: {'precision': 0.8191960332920134, 'recall': 0.9140486069946651, 'f1': 0.8640268957788569, 'number': 5061}
-- Org: {'precision': 0.9199886104783599, 'recall': 0.9367932734125833, 'f1': 0.9283148972848728, 'number': 3449}
-- Per: {'precision': 0.9687377113645301, 'recall': 0.9456813819577735, 'f1': 0.9570707070707071, 'number': 5210}
-- Overall Precision: 0.9068
-- Overall Recall: 0.9324
-- Overall F1: 0.9194
-- Overall Accuracy: 0.9904
+- Loc
+  - Precision: 0.9289891395154553
+  - Recall: 0.9336691855583543
+  - F1: 0.931323283082077
+  - Number: 5955
+- Misc
+  - Precision: 0.8191960332920134
+  - Recall: 0.9140486069946651
+  - F1: 0.8640268957788569
+  - Number: 5061
+- Org
+  - Precision: 0.9199886104783599
+  - Recall: 0.9367932734125833
+  - F1: 0.9283148972848728
+  - Number: 3449
+- Per
+  - Precision: 0.9687377113645301
+  - Recall: 0.9456813819577735
+  - F1: 0.9570707070707071
+  - Number: 5210
+- Overall
+  - Precision: 0.9068
+  - Recall: 0.9324
+  - F1: 0.9194
+  - Accuracy: 0.9904
 
 ## Model description
 
@@ -60,11 +77,11 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Loc                                                                                                       | Misc                                                                                                      | Org                                                                                                       | Per                                                                                                       | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.1119        | 1.0   | 5795  | 0.1067          | {'precision': 0.9053637984119267, 'recall': 0.9382031905961377, 'f1': 0.9214910110506349, 'number': 5955} | {'precision': 0.7967393230551125, 'recall': 0.8883619837976684, 'f1': 0.8400597907324365, 'number': 5061} | {'precision': 0.911225658648339, 'recall': 0.9225862568860539, 'f1': 0.9168707679008787, 'number': 3449}  | {'precision': 0.958470156461271, 'recall': 0.9523992322456813, 'f1': 0.9554250505439492, 'number': 5210}  | 0.8899            | 0.9264         | 0.9078     | 0.9887           |
-| 0.0724        | 2.0   | 11590 | 0.0949          | {'precision': 0.9289891395154553, 'recall': 0.9336691855583543, 'f1': 0.931323283082077, 'number': 5955}  | {'precision': 0.8191960332920134, 'recall': 0.9140486069946651, 'f1': 0.8640268957788569, 'number': 5061} | {'precision': 0.9199886104783599, 'recall': 0.9367932734125833, 'f1': 0.9283148972848728, 'number': 3449} | {'precision': 0.9687377113645301, 'recall': 0.9456813819577735, 'f1': 0.9570707070707071, 'number': 5210} | 0.9068            | 0.9324         | 0.9194     | 0.9904           |
-
+| Training Loss | Epoch | Step  | Validation Loss | Loc Precision | Loc Recall | Loc F1 | Loc Number | Misc Precision | Misc Recall | Misc F1 | Misc Number | Org Precision | Org Recall | Org F1 | Org Number | Per Precision | Per Recall | Per F1 | Per Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:-----:|:--------------:|:-----------------:|:-------------:|:------------:|:--------------:|:-----------------:|:--------------:|:----------:|:--------:|:--------:|:----------:|:-----------:|:----------:|:----------:|:----------:|:---------:|:----------:|:---------:|:-------:|:----------:|:---------:|
+| 0.1119        | 1.0   | 5795  | 0.1067 | 0.9054 | 0.9382 | 0.9215 | 5955 | 0.7967 | 0.8884 | 0.8401 | 5061 | 0.9112 | 0.9226 | 0.9169 | 3449 | 0.9585 | 0.9524 | 0.9554 | 5210 | 0.8899 | 0.9264 | 0.9078 | 0.9887 |
+| 0.0724        | 2.0   | 11590 | 0.0949 | 0.9290 | 0.9337 | 0.9313 | 5955 | 0.8192 | 0.9140 | 0.8640 | 5061 | 0.9200 | 0.9368 | 0.9283 | 3449 | 0.9687 | 0.9457 | 0.9571 | 5210 | 0.9068 | 0.9324 | 0.9194 | 0.9904 |
+* All values in the above chart are rounded to the nearest ten-thousandths.
 
 ### Framework versions