Update README.md

README.md

@@ -18,21 +18,37 @@ metrics:
 pipeline_tag: token-classification
 ---
 
-
 # roberta-base-finetuned-WikiNeural
 
 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base).
 
 It achieves the following results on the evaluation set:
 - Loss: 0.0871
-- Loc: {'precision': 0.9276567437219359, 'recall': 0.9366918555835433, 'f1': 0.9321524064171123, 'number': 5955}
-- Misc: {'precision': 0.8334231805929919, 'recall': 0.916419679905157, 'f1': 0.872953133822699, 'number': 5061}
-- Org: {'precision': 0.9296179258833669, 'recall': 0.9382429689765149, 'f1': 0.9339105339105339, 'number': 3449}
-- Per: {'precision': 0.9688723570869224, 'recall': 0.9499040307101727, 'f1': 0.9592944369063772, 'number': 5210}
-- Overall Precision: 0.9124
-- Overall Recall: 0.9352
-- Overall F1: 0.9237
-- Overall Accuracy: 0.9910
+- Loc
+  - Precision: 0.9276567437219359
+  - Recall: 0.9366918555835433
+  - F1: 0.9321524064171123
+  - Number: 5955
+- Misc
+  - Precision: 0.8334231805929919
+  - Recall: 0.916419679905157
+  - F1: 0.872953133822699
+  - Number: 5061
+- Org
+  - Precision: 0.9296179258833669
+  - Recall: 0.9382429689765149
+  - F1: 0.9339105339105339
+  - Number: 3449
+- Per
+  - Precision: 0.9688723570869224
+  - Recall: 0.9499040307101727
+  - F1: 0.9592944369063772
+  - Number: 5210
+- Overall
+  - Precision: 0.9124
+  - Recall: 0.9352
+  - F1: 0.9237
+  - Accuracy: 0.9910
 
 ## Model description
 
@@ -61,11 +77,12 @@ 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 |
-|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.1086        | 1.0   | 5795  | 0.1001          | {'precision': 0.9148971193415638, 'recall': 0.9333333333333333, 'f1': 0.9240232751454697, 'number': 5955} | {'precision': 0.8157800785433774, 'recall': 0.9029836000790358, 'f1': 0.8571696520678983, 'number': 5061} | {'precision': 0.9133903133903134, 'recall': 0.9295447955929255, 'f1': 0.9213967524069551, 'number': 3449} | {'precision': 0.9642018779342723, 'recall': 0.9460652591170825, 'f1': 0.9550474714202672, 'number': 5210} | 0.8997            | 0.9282         | 0.9137     | 0.9896           |
-| 0.0727        | 2.0   | 11590 | 0.0871          | {'precision': 0.9276567437219359, 'recall': 0.9366918555835433, 'f1': 0.9321524064171123, 'number': 5955} | {'precision': 0.8334231805929919, 'recall': 0.916419679905157, 'f1': 0.872953133822699, 'number': 5061}   | {'precision': 0.9296179258833669, 'recall': 0.9382429689765149, 'f1': 0.9339105339105339, 'number': 3449} | {'precision': 0.9688723570869224, 'recall': 0.9499040307101727, 'f1': 0.9592944369063772, 'number': 5210} | 0.9124            | 0.9352         | 0.9237     | 0.9910           |
+| 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 |
+|:-------------:|:-----:|:-----:|:----------:|:-----------:|:------------:|:------------:|:------------:|:-----------------:|:--------------:|:----------:|:--------:|:----------:|:--------:|:----------:|:--------:|:----------:|:--------:|:----------:|:--------:|:----------:|:--------:|:----------:|:--------:|
+| 0.1086   | 1.0   | 5795  | 0.1001  | 0.9149 | 0.9333 | 0.9240 | 5955 | 0.8158 | 0.9030 | 0.8572 | 5061 | 0.9134 | 0.9295 | 0.9214 | 3449 | 0.9642 | 0.9461 | 0.9550 | 5210 | 0.8997 | 0.9282 | 0.9137 | 0.9896 |
+| 0.0727   | 2.0   | 11590 | 0.0871  | 0.9277 | 0.9367 | 0.9325 | 5955 | 0.8334 | 0.9164 | 0.8730 | 5061 | 0.9296 | 0.9382 | 0.9339 | 3449 | 0.9689 | 0.9499 | 0.9593 | 5210 | 0.9124 | 0.9352 | 0.9237 | 0.9910 |
 
+* All values in the cahrt above are rounded to the nearest ten-thousandths.
 
 ### Framework versions