Update README.md

README.md

@@ -23,14 +23,31 @@ pipeline_tag: token-classification
 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased).
 It achieves the following results on the evaluation set:
 - Loss: 0.0881
-- Loc: {'precision': 0.9282034236330398, 'recall': 0.9378673383711167, 'f1': 0.9330103575008353, 'number': 5955}
-- Misc: {'precision': 0.8336608897623727, 'recall': 0.9219521833629718, 'f1': 0.8755864139613436, 'number': 5061}
-- Org: {'precision': 0.9351851851851852, 'recall': 0.9370832125253696, 'f1': 0.9361332367849385, 'number': 3449}
-- Per: {'precision': 0.9728037566034045, 'recall': 0.9543186180422265, 'f1': 0.9634725317314214, 'number': 5210}
-- Overall Precision: 0.9145
-- Overall Recall: 0.9380
-- Overall F1: 0.9261
-- Overall Accuracy: 0.9912
+- Loc
+  - Precision: 0.9282034236330398
+  - Recall: 0.9378673383711167
+  - F1: 0.9330103575008353
+  - Number: 5955
+- Misc
+  - Precision: 0.8336608897623727
+  - Rrecall: 0.9219521833629718
+  - F1: 0.8755864139613436
+  - Number: 5061
+- Org
+  - Precision: 0.9351851851851852
+  - Recall: 0.9370832125253696
+  - F1: 0.9361332367849385
+  - Number: 3449
+- Per
+  - Precision: 0.9728037566034045
+  - Recall: 0.9543186180422265
+  - F1: 0.9634725317314214
+  - Number: 5210
+- Overall
+  - Precision: 0.9145
+  - Recall: 0.9380
+  - F1: 0.9261
+  - Accuracy: 0.9912
 
 ## Model description
 
@@ -59,11 +76,12 @@ 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 |
-|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.1           | 1.0   | 5795  | 0.0943          | {'precision': 0.9075480846937126, 'recall': 0.9429051217464316, 'f1': 0.9248888156811068, 'number': 5955} | {'precision': 0.8320190720704199, 'recall': 0.8964631495751828, 'f1': 0.8630397565151225, 'number': 5061} | {'precision': 0.9151428571428571, 'recall': 0.9286749782545666, 'f1': 0.9218592603252267, 'number': 3449} | {'precision': 0.9683036587751908, 'recall': 0.9499040307101727, 'f1': 0.9590155992636372, 'number': 5210} | 0.9039            | 0.9303         | 0.9169     | 0.9901           |
-| 0.0578        | 2.0   | 11590 | 0.0881          | {'precision': 0.9282034236330398, 'recall': 0.9378673383711167, 'f1': 0.9330103575008353, 'number': 5955} | {'precision': 0.8336608897623727, 'recall': 0.9219521833629718, 'f1': 0.8755864139613436, 'number': 5061} | {'precision': 0.9351851851851852, 'recall': 0.9370832125253696, 'f1': 0.9361332367849385, 'number': 3449} | {'precision': 0.9728037566034045, 'recall': 0.9543186180422265, 'f1': 0.9634725317314214, 'number': 5210} | 0.9145            | 0.9380         | 0.9261     | 0.9912           |
+| 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 |
+|:-------------:|:-----:|:-----:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:------------:|:-----------:|:------------:|:--------:|:----------:|:--------:|:----------:|:---:|
+| 0.1           | 1.0   | 5795  | 0.0943 | 0.9075 | 0.9429 | 0.9249 | 5955 | 0.8320 | 0.8965 | 0.8630 | 5061 | 0.9151 | 0.9287 | 0.9219 | 3449 | 0.9683 | 0.9499 | 0.9590 | 5210 | 0.9039 | 0.9303 | 0.9169 | 0.9901 |
+| 0.0578        | 2.0   | 11590 | 0.0881 | 0.9282 | 0.9379 | 0.9330 | 5955 | 0.8337 | 0.9220 | 0.8756 | 5061 | 0.9352 | 0.9371 | 0.9361 | 3449 | 0.9728 | 0.9543 | 0.9635 | 5210 | 0.9145 | 0.9380 | 0.9261 | 0.9912 |
 
+* All values in the chart above are rounded to the nearest ten-thousandth.
 
 ### Framework versions