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: 0.0947
-- Loc: {'precision': 0.786833855799373, 'recall': 0.7943037974683544, 'f1': 0.7905511811023622, 'number': 316}
-- Misc: {'precision': 0.52, 'recall': 0.4642857142857143, 'f1': 0.49056603773584906, 'number': 56}
-- Org: {'precision': 0.8063492063492064, 'recall': 0.8382838283828383, 'f1': 0.8220064724919095, 'number': 303}
-- Per: {'precision': 0.8646864686468647, 'recall': 0.8136645962732919, 'f1': 0.8384000000000001, 'number': 322}
-- Overall Precision: 0.8034
-- Overall Recall: 0.7954
-- Overall F1: 0.7994
-- Overall Accuracy: 0.9840
+- Loss: 0.1177
+- Loc: {'precision': 0.7309417040358744, 'recall': 0.7546296296296297, 'f1': 0.7425968109339408, 'number': 216}
+- Misc: {'precision': 0.5862068965517241, 'recall': 0.425, 'f1': 0.4927536231884058, 'number': 40}
+- Org: {'precision': 0.8333333333333334, 'recall': 0.825, 'f1': 0.8291457286432161, 'number': 200}
+- Per: {'precision': 0.7823834196891192, 'recall': 0.7704081632653061, 'f1': 0.776349614395887, 'number': 196}
+- Overall Precision: 0.7714
+- Overall Recall: 0.7607
+- Overall F1: 0.7660
+- Overall Accuracy: 0.9812
 
 ## Model description
 
@@ -51,18 +51,18 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| No log        | 1.0   | 476  | 0.0653          | {'precision': 0.6576819407008087, 'recall': 0.7721518987341772, 'f1': 0.7103347889374091, 'number': 316} | {'precision': 0.6785714285714286, 'recall': 0.3392857142857143, 'f1': 0.45238095238095244, 'number': 56}   | {'precision': 0.7062314540059347, 'recall': 0.7854785478547854, 'f1': 0.74375, 'number': 303}            | {'precision': 0.7735849056603774, 'recall': 0.7639751552795031, 'f1': 0.76875, 'number': 322}            | 0.7087            | 0.7492         | 0.7284     | 0.9809           |
-| 0.0974        | 2.0   | 952  | 0.0602          | {'precision': 0.7537091988130564, 'recall': 0.8037974683544303, 'f1': 0.777947932618683, 'number': 316}  | {'precision': 0.43103448275862066, 'recall': 0.44642857142857145, 'f1': 0.43859649122807015, 'number': 56} | {'precision': 0.8316151202749141, 'recall': 0.7986798679867987, 'f1': 0.8148148148148148, 'number': 303} | {'precision': 0.8338870431893688, 'recall': 0.7795031055900621, 'f1': 0.8057784911717496, 'number': 322} | 0.7822            | 0.7743         | 0.7782     | 0.9833           |
-| 0.0431        | 3.0   | 1428 | 0.0687          | {'precision': 0.7623188405797101, 'recall': 0.8322784810126582, 'f1': 0.7957639939485627, 'number': 316} | {'precision': 0.5853658536585366, 'recall': 0.42857142857142855, 'f1': 0.4948453608247423, 'number': 56}   | {'precision': 0.8056426332288401, 'recall': 0.8481848184818482, 'f1': 0.8263665594855306, 'number': 303} | {'precision': 0.8140243902439024, 'recall': 0.8291925465838509, 'f1': 0.8215384615384614, 'number': 322} | 0.7851            | 0.8134         | 0.7990     | 0.9843           |
-| 0.0227        | 4.0   | 1904 | 0.0802          | {'precision': 0.7868852459016393, 'recall': 0.759493670886076, 'f1': 0.7729468599033816, 'number': 316}  | {'precision': 0.6153846153846154, 'recall': 0.42857142857142855, 'f1': 0.5052631578947369, 'number': 56}   | {'precision': 0.7724550898203593, 'recall': 0.8514851485148515, 'f1': 0.8100470957613815, 'number': 303} | {'precision': 0.8509933774834437, 'recall': 0.7981366459627329, 'f1': 0.8237179487179489, 'number': 322} | 0.7949            | 0.7813         | 0.7881     | 0.9837           |
-| 0.0136        | 5.0   | 2380 | 0.0871          | {'precision': 0.7934426229508197, 'recall': 0.7658227848101266, 'f1': 0.7793880837359098, 'number': 316} | {'precision': 0.5106382978723404, 'recall': 0.42857142857142855, 'f1': 0.46601941747572817, 'number': 56}  | {'precision': 0.8406779661016949, 'recall': 0.8184818481848185, 'f1': 0.8294314381270903, 'number': 303} | {'precision': 0.8662207357859532, 'recall': 0.8043478260869565, 'f1': 0.8341384863123993, 'number': 322} | 0.8171            | 0.7753         | 0.7957     | 0.9839           |
-| 0.0102        | 6.0   | 2856 | 0.0863          | {'precision': 0.7980132450331126, 'recall': 0.7626582278481012, 'f1': 0.7799352750809061, 'number': 316} | {'precision': 0.6, 'recall': 0.48214285714285715, 'f1': 0.5346534653465347, 'number': 56}                  | {'precision': 0.8508474576271187, 'recall': 0.8283828382838284, 'f1': 0.8394648829431438, 'number': 303} | {'precision': 0.8507936507936508, 'recall': 0.8322981366459627, 'f1': 0.8414442700156985, 'number': 322} | 0.8224            | 0.7894         | 0.8055     | 0.9847           |
-| 0.008         | 7.0   | 3332 | 0.0935          | {'precision': 0.7896440129449838, 'recall': 0.7721518987341772, 'f1': 0.7807999999999999, 'number': 316} | {'precision': 0.6136363636363636, 'recall': 0.48214285714285715, 'f1': 0.54, 'number': 56}                 | {'precision': 0.8456375838926175, 'recall': 0.8316831683168316, 'f1': 0.8386023294509152, 'number': 303} | {'precision': 0.8444444444444444, 'recall': 0.8260869565217391, 'f1': 0.8351648351648352, 'number': 322} | 0.8168            | 0.7914         | 0.8039     | 0.9844           |
-| 0.0051        | 8.0   | 3808 | 0.0967          | {'precision': 0.7866242038216561, 'recall': 0.7816455696202531, 'f1': 0.7841269841269841, 'number': 316} | {'precision': 0.6222222222222222, 'recall': 0.5, 'f1': 0.5544554455445545, 'number': 56}                   | {'precision': 0.8445945945945946, 'recall': 0.8250825082508251, 'f1': 0.8347245409015025, 'number': 303} | {'precision': 0.8673469387755102, 'recall': 0.7919254658385093, 'f1': 0.827922077922078, 'number': 322}  | 0.8219            | 0.7823         | 0.8016     | 0.9842           |
-| 0.0048        | 9.0   | 4284 | 0.0945          | {'precision': 0.789308176100629, 'recall': 0.7943037974683544, 'f1': 0.7917981072555205, 'number': 316}  | {'precision': 0.5306122448979592, 'recall': 0.4642857142857143, 'f1': 0.49523809523809526, 'number': 56}   | {'precision': 0.8089171974522293, 'recall': 0.8382838283828383, 'f1': 0.8233387358184765, 'number': 303} | {'precision': 0.8708609271523179, 'recall': 0.8167701863354038, 'f1': 0.842948717948718, 'number': 322}  | 0.8077            | 0.7964         | 0.8020     | 0.9844           |
-| 0.0033        | 10.0  | 4760 | 0.0947          | {'precision': 0.786833855799373, 'recall': 0.7943037974683544, 'f1': 0.7905511811023622, 'number': 316}  | {'precision': 0.52, 'recall': 0.4642857142857143, 'f1': 0.49056603773584906, 'number': 56}                 | {'precision': 0.8063492063492064, 'recall': 0.8382838283828383, 'f1': 0.8220064724919095, 'number': 303} | {'precision': 0.8646864686468647, 'recall': 0.8136645962732919, 'f1': 0.8384000000000001, 'number': 322} | 0.8034            | 0.7954         | 0.7994     | 0.9840           |
+| Training Loss | Epoch | Step | Validation Loss | Loc                                                                                                      | Misc                                                                                                      | Org                                                                                                      | Per                                                                                                      | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| No log        | 1.0   | 408  | 0.0602          | {'precision': 0.6894977168949772, 'recall': 0.7330097087378641, 'f1': 0.7105882352941175, 'number': 206} | {'precision': 0.8461538461538461, 'recall': 0.2972972972972973, 'f1': 0.44000000000000006, 'number': 37}  | {'precision': 0.7472527472527473, 'recall': 0.7472527472527473, 'f1': 0.7472527472527473, 'number': 182} | {'precision': 0.8195876288659794, 'recall': 0.7571428571428571, 'f1': 0.787128712871287, 'number': 210}  | 0.7516            | 0.7197         | 0.7353     | 0.9830           |
+| 0.0903        | 2.0   | 816  | 0.0568          | {'precision': 0.776255707762557, 'recall': 0.8252427184466019, 'f1': 0.7999999999999999, 'number': 206}  | {'precision': 0.5217391304347826, 'recall': 0.32432432432432434, 'f1': 0.4, 'number': 37}                 | {'precision': 0.7731958762886598, 'recall': 0.8241758241758241, 'f1': 0.7978723404255318, 'number': 182} | {'precision': 0.822429906542056, 'recall': 0.8380952380952381, 'f1': 0.830188679245283, 'number': 210}   | 0.7815            | 0.8            | 0.7907     | 0.9845           |
+| 0.0357        | 3.0   | 1224 | 0.0631          | {'precision': 0.7339055793991416, 'recall': 0.8300970873786407, 'f1': 0.7790432801822323, 'number': 206} | {'precision': 0.6363636363636364, 'recall': 0.3783783783783784, 'f1': 0.4745762711864407, 'number': 37}   | {'precision': 0.7969543147208121, 'recall': 0.8626373626373627, 'f1': 0.8284960422163589, 'number': 182} | {'precision': 0.8317757009345794, 'recall': 0.8476190476190476, 'f1': 0.839622641509434, 'number': 210}  | 0.7808            | 0.8189         | 0.7994     | 0.9851           |
+| 0.0201        | 4.0   | 1632 | 0.0761          | {'precision': 0.772093023255814, 'recall': 0.8058252427184466, 'f1': 0.7885985748218527, 'number': 206}  | {'precision': 0.6, 'recall': 0.32432432432432434, 'f1': 0.4210526315789474, 'number': 37}                 | {'precision': 0.8263157894736842, 'recall': 0.8626373626373627, 'f1': 0.8440860215053764, 'number': 182} | {'precision': 0.8293838862559242, 'recall': 0.8333333333333334, 'f1': 0.8313539192399049, 'number': 210} | 0.8019            | 0.8031         | 0.8025     | 0.9846           |
+| 0.0113        | 5.0   | 2040 | 0.0745          | {'precision': 0.7477876106194691, 'recall': 0.8203883495145631, 'f1': 0.7824074074074074, 'number': 206} | {'precision': 0.4666666666666667, 'recall': 0.3783783783783784, 'f1': 0.417910447761194, 'number': 37}    | {'precision': 0.8229166666666666, 'recall': 0.8681318681318682, 'f1': 0.8449197860962567, 'number': 182} | {'precision': 0.8388625592417062, 'recall': 0.8428571428571429, 'f1': 0.840855106888361, 'number': 210}  | 0.7860            | 0.8157         | 0.8006     | 0.9849           |
+| 0.0113        | 6.0   | 2448 | 0.0815          | {'precision': 0.7654867256637168, 'recall': 0.8398058252427184, 'f1': 0.8009259259259259, 'number': 206} | {'precision': 0.43333333333333335, 'recall': 0.35135135135135137, 'f1': 0.3880597014925374, 'number': 37} | {'precision': 0.8253968253968254, 'recall': 0.8571428571428571, 'f1': 0.8409703504043127, 'number': 182} | {'precision': 0.8673469387755102, 'recall': 0.8095238095238095, 'f1': 0.8374384236453202, 'number': 210} | 0.7988            | 0.8063         | 0.8025     | 0.9844           |
+| 0.0085        | 7.0   | 2856 | 0.0850          | {'precision': 0.7579908675799086, 'recall': 0.8058252427184466, 'f1': 0.7811764705882352, 'number': 206} | {'precision': 0.5416666666666666, 'recall': 0.35135135135135137, 'f1': 0.4262295081967213, 'number': 37}  | {'precision': 0.828125, 'recall': 0.8736263736263736, 'f1': 0.8502673796791443, 'number': 182}           | {'precision': 0.8805970149253731, 'recall': 0.8428571428571429, 'f1': 0.8613138686131387, 'number': 210} | 0.8097            | 0.8110         | 0.8104     | 0.9853           |
+| 0.0045        | 8.0   | 3264 | 0.0846          | {'precision': 0.7321428571428571, 'recall': 0.7961165048543689, 'f1': 0.7627906976744185, 'number': 206} | {'precision': 0.4642857142857143, 'recall': 0.35135135135135137, 'f1': 0.39999999999999997, 'number': 37} | {'precision': 0.8172043010752689, 'recall': 0.8351648351648352, 'f1': 0.8260869565217392, 'number': 182} | {'precision': 0.8756218905472637, 'recall': 0.8380952380952381, 'f1': 0.856447688564477, 'number': 210}  | 0.7903            | 0.7953         | 0.7928     | 0.9847           |
+| 0.0044        | 9.0   | 3672 | 0.0845          | {'precision': 0.7614678899082569, 'recall': 0.8058252427184466, 'f1': 0.7830188679245284, 'number': 206} | {'precision': 0.48148148148148145, 'recall': 0.35135135135135137, 'f1': 0.40625, 'number': 37}            | {'precision': 0.8297872340425532, 'recall': 0.8571428571428571, 'f1': 0.8432432432432433, 'number': 182} | {'precision': 0.8811881188118812, 'recall': 0.8476190476190476, 'f1': 0.8640776699029127, 'number': 210} | 0.8079            | 0.8079         | 0.8079     | 0.9854           |
+| 0.0031        | 10.0  | 4080 | 0.0855          | {'precision': 0.7568807339449541, 'recall': 0.8009708737864077, 'f1': 0.7783018867924528, 'number': 206} | {'precision': 0.4482758620689655, 'recall': 0.35135135135135137, 'f1': 0.393939393939394, 'number': 37}   | {'precision': 0.8297872340425532, 'recall': 0.8571428571428571, 'f1': 0.8432432432432433, 'number': 182} | {'precision': 0.8855721393034826, 'recall': 0.8476190476190476, 'f1': 0.8661800486618005, 'number': 210} | 0.8050            | 0.8063         | 0.8057     | 0.9852           |
 
 
 ### Framework versions