dile3/biobert-ner-ncbi

README.md

@@ -15,15 +15,15 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0313
+- Loss: 0.0386
 - Compositemention: {'precision': 0.8, 'recall': 0.9142857142857143, 'f1': 0.8533333333333333, 'number': 35}
-- Diseaseclass: {'precision': 0.6180555555555556, 'recall': 0.7063492063492064, 'f1': 0.6592592592592594, 'number': 126}
-- Modifier: {'precision': 0.7419354838709677, 'recall': 0.8598130841121495, 'f1': 0.7965367965367967, 'number': 214}
-- Specificdisease: {'precision': 0.8409638554216867, 'recall': 0.8470873786407767, 'f1': 0.8440145102781136, 'number': 412}
-- Overall Precision: 0.7721
-- Overall Recall: 0.8310
-- Overall F1: 0.8005
-- Overall Accuracy: 0.9945
+- Diseaseclass: {'precision': 0.5341614906832298, 'recall': 0.6825396825396826, 'f1': 0.5993031358885017, 'number': 126}
+- Modifier: {'precision': 0.7014925373134329, 'recall': 0.8785046728971962, 'f1': 0.7800829875518672, 'number': 214}
+- Specificdisease: {'precision': 0.8254716981132075, 'recall': 0.8495145631067961, 'f1': 0.8373205741626795, 'number': 412}
+- Overall Precision: 0.7346
+- Overall Recall: 0.8335
+- Overall F1: 0.7810
+- Overall Accuracy: 0.9934
 
 ## Model description
 
@@ -48,17 +48,18 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
-- num_epochs: 5
+- num_epochs: 20
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Compositemention                                                                                        | Diseaseclass                                                                                             | Modifier                                                                                                 | Specificdisease                                                                                          | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.009         | 1.0   | 359  | 0.0256          | {'precision': 0.7142857142857143, 'recall': 0.8571428571428571, 'f1': 0.7792207792207793, 'number': 35} | {'precision': 0.5939849624060151, 'recall': 0.626984126984127, 'f1': 0.61003861003861, 'number': 126}    | {'precision': 0.7364016736401674, 'recall': 0.822429906542056, 'f1': 0.7770419426048566, 'number': 214}  | {'precision': 0.7966507177033493, 'recall': 0.808252427184466, 'f1': 0.8024096385542169, 'number': 412}  | 0.7428            | 0.7853         | 0.7634     | 0.9939           |
-| 0.0058        | 2.0   | 718  | 0.0264          | {'precision': 0.717948717948718, 'recall': 0.8, 'f1': 0.7567567567567569, 'number': 35}                 | {'precision': 0.5460122699386503, 'recall': 0.7063492063492064, 'f1': 0.6159169550173009, 'number': 126} | {'precision': 0.7627118644067796, 'recall': 0.8411214953271028, 'f1': 0.7999999999999999, 'number': 214} | {'precision': 0.8114558472553699, 'recall': 0.8252427184466019, 'f1': 0.8182912154031287, 'number': 412} | 0.7433            | 0.8094         | 0.7749     | 0.9939           |
-| 0.0033        | 3.0   | 1077 | 0.0291          | {'precision': 0.7560975609756098, 'recall': 0.8857142857142857, 'f1': 0.8157894736842105, 'number': 35} | {'precision': 0.5894039735099338, 'recall': 0.7063492063492064, 'f1': 0.6425992779783394, 'number': 126} | {'precision': 0.7835497835497836, 'recall': 0.8457943925233645, 'f1': 0.8134831460674157, 'number': 214} | {'precision': 0.8385542168674699, 'recall': 0.8446601941747572, 'f1': 0.8415961305925029, 'number': 412} | 0.7745            | 0.8247         | 0.7988     | 0.9943           |
-| 0.0018        | 4.0   | 1436 | 0.0315          | {'precision': 0.7804878048780488, 'recall': 0.9142857142857143, 'f1': 0.8421052631578947, 'number': 35} | {'precision': 0.647887323943662, 'recall': 0.7301587301587301, 'f1': 0.6865671641791045, 'number': 126}  | {'precision': 0.7695473251028807, 'recall': 0.8738317757009346, 'f1': 0.8183807439824945, 'number': 214} | {'precision': 0.8459657701711492, 'recall': 0.8398058252427184, 'f1': 0.8428745432399514, 'number': 412} | 0.7868            | 0.8348         | 0.8101     | 0.9946           |
-| 0.0012        | 5.0   | 1795 | 0.0313          | {'precision': 0.8, 'recall': 0.9142857142857143, 'f1': 0.8533333333333333, 'number': 35}                | {'precision': 0.6180555555555556, 'recall': 0.7063492063492064, 'f1': 0.6592592592592594, 'number': 126} | {'precision': 0.7419354838709677, 'recall': 0.8598130841121495, 'f1': 0.7965367965367967, 'number': 214} | {'precision': 0.8409638554216867, 'recall': 0.8470873786407767, 'f1': 0.8440145102781136, 'number': 412} | 0.7721            | 0.8310         | 0.8005     | 0.9945           |
+| 0.005         | 1.0   | 359  | 0.0271          | {'precision': 0.725, 'recall': 0.8285714285714286, 'f1': 0.7733333333333333, 'number': 35}              | {'precision': 0.5263157894736842, 'recall': 0.7142857142857143, 'f1': 0.6060606060606061, 'number': 126} | {'precision': 0.7198443579766537, 'recall': 0.8644859813084113, 'f1': 0.7855626326963906, 'number': 214} | {'precision': 0.8103448275862069, 'recall': 0.7985436893203883, 'f1': 0.80440097799511, 'number': 412}   | 0.7243            | 0.8043         | 0.7622     | 0.9939           |
+| 0.0049        | 2.0   | 718  | 0.0277          | {'precision': 0.775, 'recall': 0.8857142857142857, 'f1': 0.8266666666666667, 'number': 35}              | {'precision': 0.5891472868217055, 'recall': 0.6031746031746031, 'f1': 0.596078431372549, 'number': 126}  | {'precision': 0.7195121951219512, 'recall': 0.8271028037383178, 'f1': 0.7695652173913042, 'number': 214} | {'precision': 0.7707423580786026, 'recall': 0.8567961165048543, 'f1': 0.8114942528735631, 'number': 412} | 0.7297            | 0.8094         | 0.7675     | 0.9934           |
+| 0.0031        | 3.0   | 1077 | 0.0330          | {'precision': 0.7142857142857143, 'recall': 0.8571428571428571, 'f1': 0.7792207792207793, 'number': 35} | {'precision': 0.4939759036144578, 'recall': 0.6507936507936508, 'f1': 0.5616438356164383, 'number': 126} | {'precision': 0.7368421052631579, 'recall': 0.8504672897196262, 'f1': 0.789587852494577, 'number': 214}  | {'precision': 0.8076923076923077, 'recall': 0.866504854368932, 'f1': 0.8360655737704917, 'number': 412}  | 0.7258            | 0.8272         | 0.7732     | 0.9935           |
+| 0.0008        | 4.0   | 1436 | 0.0324          | {'precision': 0.7567567567567568, 'recall': 0.8, 'f1': 0.7777777777777778, 'number': 35}                | {'precision': 0.6014492753623188, 'recall': 0.6587301587301587, 'f1': 0.6287878787878789, 'number': 126} | {'precision': 0.746938775510204, 'recall': 0.8551401869158879, 'f1': 0.7973856209150327, 'number': 214}  | {'precision': 0.8389423076923077, 'recall': 0.8470873786407767, 'f1': 0.8429951690821257, 'number': 412} | 0.7691            | 0.8170         | 0.7924     | 0.9940           |
+| 0.0019        | 5.0   | 1795 | 0.0314          | {'precision': 0.7804878048780488, 'recall': 0.9142857142857143, 'f1': 0.8421052631578947, 'number': 35} | {'precision': 0.6356589147286822, 'recall': 0.6507936507936508, 'f1': 0.6431372549019608, 'number': 126} | {'precision': 0.7615062761506276, 'recall': 0.8504672897196262, 'f1': 0.8035320088300221, 'number': 214} | {'precision': 0.8148148148148148, 'recall': 0.8543689320388349, 'f1': 0.8341232227488151, 'number': 412} | 0.7705            | 0.8234         | 0.7961     | 0.9939           |
+| 0.0017        | 6.0   | 2154 | 0.0386          | {'precision': 0.8, 'recall': 0.9142857142857143, 'f1': 0.8533333333333333, 'number': 35}                | {'precision': 0.5341614906832298, 'recall': 0.6825396825396826, 'f1': 0.5993031358885017, 'number': 126} | {'precision': 0.7014925373134329, 'recall': 0.8785046728971962, 'f1': 0.7800829875518672, 'number': 214} | {'precision': 0.8254716981132075, 'recall': 0.8495145631067961, 'f1': 0.8373205741626795, 'number': 412} | 0.7346            | 0.8335         | 0.7810     | 0.9934           |
 
 
 ### Framework versions

model.safetensors

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 size 430929740

training_args.bin

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 size 5240