update model card README.md

README.md

@@ -14,15 +14,15 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1879
-- Loc: {'precision': 0.6144200626959248, 'recall': 0.5714285714285714, 'f1': 0.5921450151057402, 'number': 686}
-- Misc: {'precision': 0.6759708737864077, 'recall': 0.6975579211020664, 'f1': 0.6865947611710324, 'number': 1597}
-- Org: {'precision': 0.6231884057971014, 'recall': 0.6417910447761194, 'f1': 0.6323529411764706, 'number': 268}
-- Per: {'precision': 0.6520963425512935, 'recall': 0.7567287784679089, 'f1': 0.7005270723526592, 'number': 966}
-- Overall Precision: 0.6541
-- Overall Recall: 0.6850
-- Overall F1: 0.6692
-- Overall Accuracy: 0.9400
+- Loss: 0.1522
+- Loc: {'precision': 0.7952488687782805, 'recall': 0.703, 'f1': 0.7462845010615711, 'number': 1000}
+- Misc: {'precision': 0.6310931641188348, 'recall': 0.6640364188163884, 'f1': 0.6471458148476782, 'number': 3295}
+- Org: {'precision': 0.6708074534161491, 'recall': 0.6792452830188679, 'f1': 0.6749999999999999, 'number': 477}
+- Per: {'precision': 0.7778738115816768, 'recall': 0.7772020725388601, 'f1': 0.7775377969762419, 'number': 1158}
+- Overall Precision: 0.6869
+- Overall Recall: 0.6939
+- Overall F1: 0.6904
+- Overall Accuracy: 0.9567
 
 ## Model description
 
@@ -51,11 +51,11 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Er                                                                                                       | Isc                                                                                                       | Oc                                                                                                       | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.2687        | 1.0   | 654  | 0.2022          | {'precision': 0.6098294884653962, 'recall': 0.629399585921325, 'f1': 0.6194600101884871, 'number': 966}  | {'precision': 0.6234604105571847, 'recall': 0.6656230432060113, 'f1': 0.6438522107813446, 'number': 1597} | {'precision': 0.5617792421746294, 'recall': 0.4970845481049563, 'f1': 0.5274555297757154, 'number': 686} | 0.6080            | 0.6193         | 0.6136     | 0.9325           |
-| 0.1623        | 2.0   | 1308 | 0.1819          | {'precision': 0.6175523349436393, 'recall': 0.7939958592132506, 'f1': 0.6947463768115941, 'number': 966} | {'precision': 0.6879526003949967, 'recall': 0.654351909830933, 'f1': 0.6707317073170731, 'number': 1597}  | {'precision': 0.6374367622259697, 'recall': 0.5510204081632653, 'f1': 0.5910867865519938, 'number': 686} | 0.6530            | 0.6741         | 0.6633     | 0.9390           |
-| 0.128         | 3.0   | 1962 | 0.1879          | {'precision': 0.6520963425512935, 'recall': 0.7567287784679089, 'f1': 0.7005270723526592, 'number': 966} | {'precision': 0.6759708737864077, 'recall': 0.6975579211020664, 'f1': 0.6865947611710324, 'number': 1597} | {'precision': 0.6144200626959248, 'recall': 0.5714285714285714, 'f1': 0.5921450151057402, 'number': 686} | 0.6566            | 0.6885         | 0.6722     | 0.9400           |
+| Training Loss | Epoch | Step | Validation Loss | Loc                                                                                          | Misc                                                                                                      | Org                                                                                                      | Per                                                                                                       | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.1195        | 1.0   | 2044 | 0.1425          | {'precision': 0.750620347394541, 'recall': 0.605, 'f1': 0.6699889258028793, 'number': 1000}  | {'precision': 0.6498784300104203, 'recall': 0.5678300455235205, 'f1': 0.606090055069647, 'number': 3295}  | {'precision': 0.6763392857142857, 'recall': 0.6352201257861635, 'f1': 0.6551351351351351, 'number': 477} | {'precision': 0.6595744680851063, 'recall': 0.7763385146804835, 'f1': 0.7132090440301467, 'number': 1158} | 0.6692            | 0.6202         | 0.6438     | 0.9511           |
+| 0.0736        | 2.0   | 4088 | 0.1387          | {'precision': 0.7714604236343366, 'recall': 0.692, 'f1': 0.7295730100158145, 'number': 1000} | {'precision': 0.6479814115596864, 'recall': 0.6770864946889226, 'f1': 0.6622143069159989, 'number': 3295} | {'precision': 0.7018348623853211, 'recall': 0.6415094339622641, 'f1': 0.6703176341730558, 'number': 477} | {'precision': 0.7717484926787253, 'recall': 0.7737478411053541, 'f1': 0.7727468736524364, 'number': 1158} | 0.6948            | 0.6956         | 0.6952     | 0.9575           |
+| 0.0499        | 3.0   | 6132 | 0.1522          | {'precision': 0.7952488687782805, 'recall': 0.703, 'f1': 0.7462845010615711, 'number': 1000} | {'precision': 0.6310931641188348, 'recall': 0.6640364188163884, 'f1': 0.6471458148476782, 'number': 3295} | {'precision': 0.6708074534161491, 'recall': 0.6792452830188679, 'f1': 0.6749999999999999, 'number': 477} | {'precision': 0.7778738115816768, 'recall': 0.7772020725388601, 'f1': 0.7775377969762419, 'number': 1158} | 0.6869            | 0.6939         | 0.6904     | 0.9567           |
 
 
 ### Framework versions