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: 0.0716
-- Loc: {'precision': 0.7296511627906976, 'recall': 0.7943037974683544, 'f1': 0.7606060606060605, 'number': 316}
-- Misc: {'precision': 0.7857142857142857, 'recall': 0.39285714285714285, 'f1': 0.5238095238095237, 'number': 56}
-- Org: {'precision': 0.7745098039215687, 'recall': 0.7821782178217822, 'f1': 0.7783251231527093, 'number': 303}
-- Per: {'precision': 0.8176100628930818, 'recall': 0.8074534161490683, 'f1': 0.8125000000000001, 'number': 322}
-- Overall Precision: 0.7731
-- Overall Recall: 0.7723
-- Overall F1: 0.7727
-- Overall Accuracy: 0.9826
+- Loss: 0.1016
+- Loc: {'precision': 0.7560975609756098, 'recall': 0.7848101265822784, 'f1': 0.7701863354037267, 'number': 316}
+- Misc: {'precision': 0.5681818181818182, 'recall': 0.44642857142857145, 'f1': 0.5, 'number': 56}
+- Org: {'precision': 0.8184818481848185, 'recall': 0.8184818481848185, 'f1': 0.8184818481848186, 'number': 303}
+- Per: {'precision': 0.8456591639871383, 'recall': 0.8167701863354038, 'f1': 0.8309636650868879, 'number': 322}
+- Overall Precision: 0.7951
+- Overall Recall: 0.7864
+- Overall F1: 0.7907
+- Overall Accuracy: 0.9834
 
 ## Model description
 
@@ -47,15 +47,22 @@ 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 |
 |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| No log        | 1.0   | 476  | 0.0740          | {'precision': 0.6106442577030813, 'recall': 0.689873417721519, 'f1': 0.6478454680534919, 'number': 316}  | {'precision': 0.6666666666666666, 'recall': 0.2857142857142857, 'f1': 0.4, 'number': 56}                 | {'precision': 0.665680473372781, 'recall': 0.7425742574257426, 'f1': 0.7020280811232449, 'number': 303}  | {'precision': 0.7469879518072289, 'recall': 0.7701863354037267, 'f1': 0.7584097859327217, 'number': 322} | 0.6727            | 0.7091         | 0.6904     | 0.9794           |
-| 0.1185        | 2.0   | 952  | 0.0647          | {'precision': 0.7383720930232558, 'recall': 0.8037974683544303, 'f1': 0.7696969696969697, 'number': 316} | {'precision': 0.6363636363636364, 'recall': 0.375, 'f1': 0.47191011235955066, 'number': 56}              | {'precision': 0.7966101694915254, 'recall': 0.7755775577557755, 'f1': 0.785953177257525, 'number': 303}  | {'precision': 0.8158730158730159, 'recall': 0.7981366459627329, 'f1': 0.8069073783359498, 'number': 322} | 0.7771            | 0.7693         | 0.7732     | 0.9831           |
-| 0.0509        | 3.0   | 1428 | 0.0716          | {'precision': 0.7296511627906976, 'recall': 0.7943037974683544, 'f1': 0.7606060606060605, 'number': 316} | {'precision': 0.7857142857142857, 'recall': 0.39285714285714285, 'f1': 0.5238095238095237, 'number': 56} | {'precision': 0.7745098039215687, 'recall': 0.7821782178217822, 'f1': 0.7783251231527093, 'number': 303} | {'precision': 0.8176100628930818, 'recall': 0.8074534161490683, 'f1': 0.8125000000000001, 'number': 322} | 0.7731            | 0.7723         | 0.7727     | 0.9826           |
+| No log        | 1.0   | 476  | 0.0747          | {'precision': 0.6298342541436464, 'recall': 0.7215189873417721, 'f1': 0.672566371681416, 'number': 316}  | {'precision': 0.6666666666666666, 'recall': 0.2857142857142857, 'f1': 0.4, 'number': 56}                 | {'precision': 0.6743515850144092, 'recall': 0.7722772277227723, 'f1': 0.7199999999999999, 'number': 303} | {'precision': 0.7070422535211267, 'recall': 0.7795031055900621, 'f1': 0.741506646971935, 'number': 322}  | 0.6700            | 0.7312         | 0.6993     | 0.9795           |
+| 0.1172        | 2.0   | 952  | 0.0640          | {'precision': 0.7471264367816092, 'recall': 0.8227848101265823, 'f1': 0.7831325301204819, 'number': 316} | {'precision': 0.6363636363636364, 'recall': 0.375, 'f1': 0.47191011235955066, 'number': 56}              | {'precision': 0.7660256410256411, 'recall': 0.7887788778877888, 'f1': 0.7772357723577236, 'number': 303} | {'precision': 0.8138801261829653, 'recall': 0.8012422360248447, 'f1': 0.8075117370892019, 'number': 322} | 0.7703            | 0.7803         | 0.7753     | 0.9839           |
+| 0.0503        | 3.0   | 1428 | 0.0718          | {'precision': 0.707774798927614, 'recall': 0.8354430379746836, 'f1': 0.7663280116110305, 'number': 316}  | {'precision': 0.75, 'recall': 0.42857142857142855, 'f1': 0.5454545454545454, 'number': 56}               | {'precision': 0.7639751552795031, 'recall': 0.8118811881188119, 'f1': 0.7872, 'number': 303}             | {'precision': 0.8024316109422492, 'recall': 0.8198757763975155, 'f1': 0.8110599078341013, 'number': 322} | 0.7557            | 0.8004         | 0.7774     | 0.9831           |
+| 0.0273        | 4.0   | 1904 | 0.0859          | {'precision': 0.7601246105919003, 'recall': 0.7721518987341772, 'f1': 0.7660910518053375, 'number': 316} | {'precision': 0.5897435897435898, 'recall': 0.4107142857142857, 'f1': 0.4842105263157895, 'number': 56}  | {'precision': 0.75, 'recall': 0.8514851485148515, 'f1': 0.7975270479134468, 'number': 303}               | {'precision': 0.8178913738019169, 'recall': 0.7950310559006211, 'f1': 0.8062992125984252, 'number': 322} | 0.7679            | 0.7834         | 0.7756     | 0.9823           |
+| 0.0179        | 5.0   | 2380 | 0.0924          | {'precision': 0.7652733118971061, 'recall': 0.7531645569620253, 'f1': 0.759170653907496, 'number': 316}  | {'precision': 0.575, 'recall': 0.4107142857142857, 'f1': 0.47916666666666663, 'number': 56}              | {'precision': 0.8116883116883117, 'recall': 0.8250825082508251, 'f1': 0.8183306055646481, 'number': 303} | {'precision': 0.8264984227129337, 'recall': 0.8136645962732919, 'f1': 0.8200312989045383, 'number': 322} | 0.7920            | 0.7753         | 0.7836     | 0.9834           |
+| 0.0126        | 6.0   | 2856 | 0.0881          | {'precision': 0.7197640117994101, 'recall': 0.7721518987341772, 'f1': 0.7450381679389314, 'number': 316} | {'precision': 0.5333333333333333, 'recall': 0.42857142857142855, 'f1': 0.4752475247524753, 'number': 56} | {'precision': 0.8245033112582781, 'recall': 0.8217821782178217, 'f1': 0.8231404958677686, 'number': 303} | {'precision': 0.8096676737160121, 'recall': 0.8322981366459627, 'f1': 0.8208269525267994, 'number': 322} | 0.7719            | 0.7874         | 0.7795     | 0.9830           |
+| 0.0094        | 7.0   | 3332 | 0.0988          | {'precision': 0.7570977917981072, 'recall': 0.759493670886076, 'f1': 0.7582938388625592, 'number': 316}  | {'precision': 0.575, 'recall': 0.4107142857142857, 'f1': 0.47916666666666663, 'number': 56}              | {'precision': 0.8305647840531561, 'recall': 0.8250825082508251, 'f1': 0.8278145695364238, 'number': 303} | {'precision': 0.8213166144200627, 'recall': 0.8136645962732919, 'f1': 0.8174726989079563, 'number': 322} | 0.7932            | 0.7773         | 0.7852     | 0.9833           |
+| 0.0057        | 8.0   | 3808 | 0.1023          | {'precision': 0.7704402515723271, 'recall': 0.7753164556962026, 'f1': 0.7728706624605678, 'number': 316} | {'precision': 0.6153846153846154, 'recall': 0.42857142857142855, 'f1': 0.5052631578947369, 'number': 56} | {'precision': 0.8283828382838284, 'recall': 0.8283828382838284, 'f1': 0.8283828382838284, 'number': 303} | {'precision': 0.8436482084690554, 'recall': 0.8043478260869565, 'f1': 0.8235294117647061, 'number': 322} | 0.8056            | 0.7813         | 0.7933     | 0.9838           |
+| 0.0058        | 9.0   | 4284 | 0.1005          | {'precision': 0.7584097859327217, 'recall': 0.7848101265822784, 'f1': 0.7713841368584758, 'number': 316} | {'precision': 0.5555555555555556, 'recall': 0.44642857142857145, 'f1': 0.4950495049504951, 'number': 56} | {'precision': 0.8239202657807309, 'recall': 0.8184818481848185, 'f1': 0.8211920529801325, 'number': 303} | {'precision': 0.8538961038961039, 'recall': 0.8167701863354038, 'f1': 0.834920634920635, 'number': 322}  | 0.7992            | 0.7864         | 0.7927     | 0.9836           |
+| 0.0045        | 10.0  | 4760 | 0.1016          | {'precision': 0.7560975609756098, 'recall': 0.7848101265822784, 'f1': 0.7701863354037267, 'number': 316} | {'precision': 0.5681818181818182, 'recall': 0.44642857142857145, 'f1': 0.5, 'number': 56}                | {'precision': 0.8184818481848185, 'recall': 0.8184818481848185, 'f1': 0.8184818481848186, 'number': 303} | {'precision': 0.8456591639871383, 'recall': 0.8167701863354038, 'f1': 0.8309636650868879, 'number': 322} | 0.7951            | 0.7864         | 0.7907     | 0.9834           |
 
 
 ### Framework versions