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: 0.1522
-- 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
+- Loss: 0.6420
+- Loc: {'precision': 0.706140350877193, 'recall': 0.4586894586894587, 'f1': 0.5561312607944733, 'number': 1053}
+- Misc: {'precision': 0.037747920665387076, 'recall': 0.6820809248554913, 'f1': 0.07153682934222491, 'number': 173}
+- Org: {'precision': 0.6212424849699398, 'recall': 0.3125, 'f1': 0.4158283031522468, 'number': 992}
+- Per: {'precision': 0.5895458440445587, 'recall': 0.67384916748286, 'f1': 0.6288848263254113, 'number': 1021}
+- Overall Precision: 0.2920
+- Overall Recall: 0.4937
+- Overall F1: 0.3670
+- Overall Accuracy: 0.9079
 
 ## Model description
 
@@ -47,20 +47,27 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 3
+- num_epochs: 10
 
 ### Training results
 
-| 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           |
+| Training Loss | Epoch | Step  | Validation Loss | Loc                                                                                                        | Misc                                                                                                        | Org                                                                                                        | Per                                                                                                       | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.125         | 1.0   | 2033  | 0.4529          | {'precision': 0.7350565428109854, 'recall': 0.43209876543209874, 'f1': 0.5442583732057417, 'number': 1053} | {'precision': 0.03518518518518519, 'recall': 0.6589595375722543, 'f1': 0.06680339876941108, 'number': 173}  | {'precision': 0.6746666666666666, 'recall': 0.2550403225806452, 'f1': 0.37015362106803223, 'number': 992}  | {'precision': 0.6089686098654709, 'recall': 0.6650342801175319, 'f1': 0.6357677902621722, 'number': 1021} | 0.2806            | 0.4634         | 0.3496     | 0.8956           |
+| 0.0809        | 2.0   | 4066  | 0.3927          | {'precision': 0.733652312599681, 'recall': 0.4368471035137702, 'f1': 0.5476190476190477, 'number': 1053}   | {'precision': 0.03864382063434196, 'recall': 0.6127167630057804, 'f1': 0.07270233196159122, 'number': 173}  | {'precision': 0.6554307116104869, 'recall': 0.3528225806451613, 'f1': 0.45871559633027525, 'number': 992}  | {'precision': 0.561034761519806, 'recall': 0.6797257590597453, 'f1': 0.6147032772364924, 'number': 1021}  | 0.3132            | 0.4971         | 0.3842     | 0.9113           |
+| 0.0609        | 3.0   | 6099  | 0.4609          | {'precision': 0.6685714285714286, 'recall': 0.4444444444444444, 'f1': 0.5339418140330862, 'number': 1053}  | {'precision': 0.03744567034436643, 'recall': 0.6473988439306358, 'f1': 0.07079646017699115, 'number': 173}  | {'precision': 0.656964656964657, 'recall': 0.3185483870967742, 'f1': 0.4290563475899525, 'number': 992}    | {'precision': 0.5294953802416489, 'recall': 0.7296767874632712, 'f1': 0.6136738056013179, 'number': 1021} | 0.2941            | 0.5066         | 0.3722     | 0.9045           |
+| 0.0423        | 4.0   | 8132  | 0.4695          | {'precision': 0.7260940032414911, 'recall': 0.4254510921177588, 'f1': 0.5365269461077844, 'number': 1053}  | {'precision': 0.04154204054503157, 'recall': 0.7225433526011561, 'f1': 0.07856693903205532, 'number': 173}  | {'precision': 0.6585903083700441, 'recall': 0.3014112903225806, 'f1': 0.4135546334716459, 'number': 992}   | {'precision': 0.552366175329713, 'recall': 0.6973555337904016, 'f1': 0.6164502164502165, 'number': 1021}  | 0.2950            | 0.4890         | 0.3680     | 0.9094           |
+| 0.0354        | 5.0   | 10165 | 0.5324          | {'precision': 0.6918518518518518, 'recall': 0.44349477682811017, 'f1': 0.5405092592592593, 'number': 1053} | {'precision': 0.03929273084479371, 'recall': 0.6936416184971098, 'f1': 0.07437248218159281, 'number': 173}  | {'precision': 0.6088794926004228, 'recall': 0.2903225806451613, 'f1': 0.39317406143344713, 'number': 992}  | {'precision': 0.5702054794520548, 'recall': 0.6523016650342801, 'f1': 0.6084970306075833, 'number': 1021} | 0.2870            | 0.4758         | 0.3580     | 0.9067           |
+| 0.0254        | 6.0   | 12198 | 0.5827          | {'precision': 0.684593023255814, 'recall': 0.4472934472934473, 'f1': 0.5410683515221136, 'number': 1053}   | {'precision': 0.031561996779388084, 'recall': 0.5664739884393064, 'f1': 0.05979255643685173, 'number': 173} | {'precision': 0.6134301270417423, 'recall': 0.3407258064516129, 'f1': 0.43810758263123784, 'number': 992}  | {'precision': 0.5973684210526315, 'recall': 0.6669931439764937, 'f1': 0.6302637667746414, 'number': 1021} | 0.2896            | 0.4903         | 0.3641     | 0.9046           |
+| 0.0206        | 7.0   | 14231 | 0.5821          | {'precision': 0.6886657101865137, 'recall': 0.45584045584045585, 'f1': 0.5485714285714286, 'number': 1053} | {'precision': 0.03427093436792758, 'recall': 0.6127167630057804, 'f1': 0.06491120636864667, 'number': 173}  | {'precision': 0.5944055944055944, 'recall': 0.34274193548387094, 'f1': 0.43478260869565216, 'number': 992} | {'precision': 0.5777964676198486, 'recall': 0.672869735553379, 'f1': 0.6217194570135747, 'number': 1021}  | 0.2906            | 0.4980         | 0.3670     | 0.9063           |
+| 0.0147        | 8.0   | 16264 | 0.6210          | {'precision': 0.7218045112781954, 'recall': 0.45584045584045585, 'f1': 0.5587892898719441, 'number': 1053} | {'precision': 0.03523542251325226, 'recall': 0.653179190751445, 'f1': 0.06686390532544378, 'number': 173}   | {'precision': 0.6188524590163934, 'recall': 0.30443548387096775, 'f1': 0.40810810810810816, 'number': 992} | {'precision': 0.6246362754607178, 'recall': 0.6307541625857003, 'f1': 0.6276803118908383, 'number': 1021} | 0.2855            | 0.4751         | 0.3567     | 0.9047           |
+| 0.0123        | 9.0   | 18297 | 0.6424          | {'precision': 0.7036496350364964, 'recall': 0.4577397910731244, 'f1': 0.5546605293440737, 'number': 1053}  | {'precision': 0.04029899252518687, 'recall': 0.7167630057803468, 'f1': 0.07630769230769231, 'number': 173}  | {'precision': 0.6023391812865497, 'recall': 0.31149193548387094, 'f1': 0.41063122923588036, 'number': 992} | {'precision': 0.5862068965517241, 'recall': 0.682664054848188, 'f1': 0.6307692307692307, 'number': 1021}  | 0.2950            | 0.4977         | 0.3704     | 0.9074           |
+| 0.0093        | 10.0  | 20330 | 0.6420          | {'precision': 0.706140350877193, 'recall': 0.4586894586894587, 'f1': 0.5561312607944733, 'number': 1053}   | {'precision': 0.037747920665387076, 'recall': 0.6820809248554913, 'f1': 0.07153682934222491, 'number': 173} | {'precision': 0.6212424849699398, 'recall': 0.3125, 'f1': 0.4158283031522468, 'number': 992}               | {'precision': 0.5895458440445587, 'recall': 0.67384916748286, 'f1': 0.6288848263254113, 'number': 1021}   | 0.2920            | 0.4937         | 0.3670     | 0.9079           |
 
 
 ### Framework versions
 
-- Transformers 4.28.1
+- Transformers 4.29.0
 - Pytorch 2.0.0+cu118
 - Datasets 2.12.0
 - Tokenizers 0.13.3