Update README.md

README.md

@@ -23,14 +23,31 @@ This model is a fine-tuned version of [kssteven/ibert-roberta-base](https://hugg
 
 It achieves the following results on the evaluation set:
 - Loss: 0.0878
-- Loc: {'precision': 0.9249338624338624, 'recall': 0.9393786733837112, 'f1': 0.9321003082562693, 'number': 5955}
-- Misc: {'precision': 0.8304751697034656, 'recall': 0.9185931634064414, 'f1': 0.8723144760296463, 'number': 5061}
-- Org: {'precision': 0.9283453237410072, 'recall': 0.9353435778486517, 'f1': 0.9318313113807049, 'number': 3449}
-- Per: {'precision': 0.9698098412076064, 'recall': 0.9495201535508637, 'f1': 0.9595577538551062, 'number': 5210}
-- Overall Precision: 0.9107
-- Overall Recall: 0.9360
-- Overall F1: 0.9232
-- Overall Accuracy: 0.9909
+- Loc
+  - Precision: 0.9249338624338624
+  - Recall: 0.9393786733837112
+  - F1: 0.9321003082562693
+  - Number: 5955
+- Misc
+  - Precision: 0.8304751697034656
+  - Recall: 0.9185931634064414
+  - F1: 0.8723144760296463
+  - Number: 5061
+- Org
+  - Precision: 0.9283453237410072
+  - Recall: 0.9353435778486517
+  - F1: 0.9318313113807049
+  - Number: 3449
+- Per
+  - Precision: 0.9698098412076064
+  - Recall: 0.9495201535508637
+  - F1: 0.9595577538551062
+  - Number: 5210
+- Overall
+  - Precision: 0.9107
+  - Recall: 0.9360
+  - F1: 0.9232
+  - Accuracy: 0.9909
 
 ## 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 |
-|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.1092        | 1.0   | 5795  | 0.0987          | {'precision': 0.9124507227332457, 'recall': 0.9328295549958019, 'f1': 0.9225276093996512, 'number': 5955} | {'precision': 0.8003130979300748, 'recall': 0.9091088717644734, 'f1': 0.8512488436632748, 'number': 5061} | {'precision': 0.9142857142857143, 'recall': 0.9278051609162076, 'f1': 0.9209958267376601, 'number': 3449} | {'precision': 0.9714229013693193, 'recall': 0.9395393474088292, 'f1': 0.9552151429407748, 'number': 5210} | 0.8957            | 0.9276         | 0.9114     | 0.9890           |
-| 0.0723        | 2.0   | 11590 | 0.0878          | {'precision': 0.9249338624338624, 'recall': 0.9393786733837112, 'f1': 0.9321003082562693, 'number': 5955} | {'precision': 0.8304751697034656, 'recall': 0.9185931634064414, 'f1': 0.8723144760296463, 'number': 5061} | {'precision': 0.9283453237410072, 'recall': 0.9353435778486517, 'f1': 0.9318313113807049, 'number': 3449} | {'precision': 0.9698098412076064, 'recall': 0.9495201535508637, 'f1': 0.9595577538551062, 'number': 5210} | 0.9107            | 0.9360         | 0.9232     | 0.9909           |
+| 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:|:---------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------:|:----------------:|
+| 0.1092        | 1.0   | 5795  | 0.0987          | 0.9125 | 0.9328 | 0.9225 | 5955 | 0.8003 | 0.9091 | 0.8512 | 5061 | 0.9143 | 0.9278 | 0.9210 | 3449 | 0.9714 | 0.9395 | 0.9552 | 5210 | 0.8957 | 0.9276 | 0.9114 | 0.9890 |
+| 0.0723        | 2.0   | 11590 | 0.0878          | 0.9249 | 0.9394 | 0.9321 | 5955 | 0.8305 | 0.9186 | 0.8723 | 5061 | 0.9283 | 0.9353 | 0.9318 | 3449 | 0.9698 | 0.9495 | 0.9596 | 5210 | 0.9107 | 0.9360 | 0.9232 | 0.9909 |
 
+* All values in the above chart arerounded to nearest ten-thousandth.
 
 ### Framework versions