dile3/biobert-ner-ncbi

README.md

@@ -17,15 +17,15 @@ 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 ncbi_disease dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1177
-- Compositemention: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 35}
-- Diseaseclass: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 126}
-- Modifier: {'precision': 0.07936507936507936, 'recall': 0.023474178403755867, 'f1': 0.03623188405797101, 'number': 213}
-- Specificdisease: {'precision': 0.4317295188556567, 'recall': 0.8058252427184466, 'f1': 0.5622353937341237, 'number': 412}
-- Overall Precision: 0.3865
-- Overall Recall: 0.4288
-- Overall F1: 0.4065
-- Overall Accuracy: 0.9631
+- Loss: 0.0557
+- Compositemention: {'precision': 0.2765957446808511, 'recall': 0.37142857142857144, 'f1': 0.3170731707317073, 'number': 35}
+- Diseaseclass: {'precision': 0.5, 'recall': 0.6031746031746031, 'f1': 0.5467625899280575, 'number': 126}
+- Modifier: {'precision': 0.7665198237885462, 'recall': 0.8169014084507042, 'f1': 0.7909090909090909, 'number': 213}
+- Specificdisease: {'precision': 0.75625, 'recall': 0.8810679611650486, 'f1': 0.81390134529148, 'number': 412}
+- Overall Precision: 0.6909
+- Overall Recall: 0.7964
+- Overall F1: 0.7400
+- Overall Accuracy: 0.9854
 
 ## Model description
 
@@ -45,20 +45,22 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
+- train_batch_size: 8
+- eval_batch_size: 8
 - 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: 3
+- num_epochs: 5
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Compositemention                                           | Diseaseclass                                                | Modifier                                                                                                     | Specificdisease                                                                                          | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.2824        | 1.0   | 38   | 0.1741          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 35} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 126} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 213}                                                  | {'precision': 0.3168724279835391, 'recall': 0.5606796116504854, 'f1': 0.4049079754601227, 'number': 412} | 0.3164            | 0.2939         | 0.3047     | 0.9514           |
-| 0.1492        | 2.0   | 76   | 0.1292          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 35} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 126} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 213}                                                  | {'precision': 0.423956931359354, 'recall': 0.7645631067961165, 'f1': 0.5454545454545455, 'number': 412}  | 0.3933            | 0.4008         | 0.3970     | 0.9613           |
-| 0.1202        | 3.0   | 114  | 0.1177          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 35} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 126} | {'precision': 0.07936507936507936, 'recall': 0.023474178403755867, 'f1': 0.03623188405797101, 'number': 213} | {'precision': 0.4317295188556567, 'recall': 0.8058252427184466, 'f1': 0.5622353937341237, 'number': 412} | 0.3865            | 0.4288         | 0.4065     | 0.9631           |
+| Training Loss | Epoch | Step | Validation Loss | Compositemention                                                                                            | Diseaseclass                                                                                              | Modifier                                                                                                 | Specificdisease                                                                                          | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0688        | 1.0   | 75   | 0.0687          | {'precision': 0.075, 'recall': 0.08571428571428572, 'f1': 0.08, 'number': 35}                               | {'precision': 0.3684210526315789, 'recall': 0.4444444444444444, 'f1': 0.4028776978417266, 'number': 126}  | {'precision': 0.5581395348837209, 'recall': 0.676056338028169, 'f1': 0.6114649681528661, 'number': 213}  | {'precision': 0.6083788706739527, 'recall': 0.8106796116504854, 'f1': 0.6951092611862643, 'number': 412} | 0.5375            | 0.6832         | 0.6017     | 0.9797           |
+| 0.0416        | 2.0   | 150  | 0.0621          | {'precision': 0.038461538461538464, 'recall': 0.02857142857142857, 'f1': 0.03278688524590164, 'number': 35} | {'precision': 0.3706896551724138, 'recall': 0.3412698412698413, 'f1': 0.3553719008264463, 'number': 126}  | {'precision': 0.6653225806451613, 'recall': 0.7746478873239436, 'f1': 0.7158351409978307, 'number': 213} | {'precision': 0.6208695652173913, 'recall': 0.866504854368932, 'f1': 0.7234042553191489, 'number': 412}  | 0.5865            | 0.7201         | 0.6465     | 0.9817           |
+| 0.0399        | 3.0   | 225  | 0.0537          | {'precision': 0.26666666666666666, 'recall': 0.34285714285714286, 'f1': 0.3, 'number': 35}                  | {'precision': 0.45390070921985815, 'recall': 0.5079365079365079, 'f1': 0.4794007490636704, 'number': 126} | {'precision': 0.7522123893805309, 'recall': 0.7981220657276995, 'f1': 0.7744874715261958, 'number': 213} | {'precision': 0.6927592954990215, 'recall': 0.8592233009708737, 'f1': 0.7670639219934994, 'number': 412} | 0.6501            | 0.7634         | 0.7022     | 0.9848           |
+| 0.0263        | 4.0   | 300  | 0.0548          | {'precision': 0.3125, 'recall': 0.42857142857142855, 'f1': 0.3614457831325301, 'number': 35}                | {'precision': 0.47096774193548385, 'recall': 0.5793650793650794, 'f1': 0.5195729537366549, 'number': 126} | {'precision': 0.776255707762557, 'recall': 0.7981220657276995, 'f1': 0.7870370370370371, 'number': 213}  | {'precision': 0.7433264887063655, 'recall': 0.8786407766990292, 'f1': 0.8053392658509455, 'number': 412} | 0.6821            | 0.7888         | 0.7316     | 0.9852           |
+| 0.0214        | 5.0   | 375  | 0.0557          | {'precision': 0.2765957446808511, 'recall': 0.37142857142857144, 'f1': 0.3170731707317073, 'number': 35}    | {'precision': 0.5, 'recall': 0.6031746031746031, 'f1': 0.5467625899280575, 'number': 126}                 | {'precision': 0.7665198237885462, 'recall': 0.8169014084507042, 'f1': 0.7909090909090909, 'number': 213} | {'precision': 0.75625, 'recall': 0.8810679611650486, 'f1': 0.81390134529148, 'number': 412}              | 0.6909            | 0.7964         | 0.7400     | 0.9854           |
 
 
 ### Framework versions

model.safetensors

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training_args.bin

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