| --- |
| base_model: dmis-lab/biobert-v1.1 |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: pretoxtm-ner |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # pretoxtm-ner |
|
|
| This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1810 |
| - Study Test: {'precision': 0.8215384615384616, 'recall': 0.8841059602649006, 'f1': 0.8516746411483254, 'number': 302} |
| - Manifestation: {'precision': 0.8041958041958042, 'recall': 0.905511811023622, 'f1': 0.8518518518518519, 'number': 127} |
| - Finding: {'precision': 0.6886657101865137, 'recall': 0.7570977917981072, 'f1': 0.7212622088655146, 'number': 634} |
| - Specimen: {'precision': 0.7944162436548223, 'recall': 0.8236842105263158, 'f1': 0.8087855297157622, 'number': 380} |
| - Dose: {'precision': 0.8647540983606558, 'recall': 0.9461883408071748, 'f1': 0.9036402569593148, 'number': 223} |
| - Dose Qualification: {'precision': 0.65, 'recall': 0.8125, 'f1': 0.7222222222222223, 'number': 32} |
| - Sex: {'precision': 0.9285714285714286, 'recall': 0.9285714285714286, 'f1': 0.9285714285714286, 'number': 84} |
| - Group: {'precision': 0.5666666666666667, 'recall': 0.6938775510204082, 'f1': 0.6238532110091742, 'number': 49} |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 3 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Study Test | Manifestation | Finding | Specimen | Dose | Dose Qualification | Sex | Group | |
| |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:| |
| | No log | 1.0 | 257 | 0.2005 | {'precision': 0.6658227848101266, 'recall': 0.8708609271523179, 'f1': 0.7546628407460545, 'number': 302} | {'precision': 0.7647058823529411, 'recall': 0.9212598425196851, 'f1': 0.8357142857142856, 'number': 127} | {'precision': 0.6425339366515838, 'recall': 0.6719242902208202, 'f1': 0.6569005397070162, 'number': 634} | {'precision': 0.7099767981438515, 'recall': 0.8052631578947368, 'f1': 0.75462392108508, 'number': 380} | {'precision': 0.8969957081545065, 'recall': 0.9372197309417041, 'f1': 0.9166666666666667, 'number': 223} | {'precision': 0.6764705882352942, 'recall': 0.71875, 'f1': 0.696969696969697, 'number': 32} | {'precision': 0.7448979591836735, 'recall': 0.8690476190476191, 'f1': 0.8021978021978022, 'number': 84} | {'precision': 0.3880597014925373, 'recall': 0.5306122448979592, 'f1': 0.4482758620689655, 'number': 49} | |
| | 0.2932 | 2.0 | 514 | 0.1689 | {'precision': 0.8170347003154574, 'recall': 0.8576158940397351, 'f1': 0.8368336025848143, 'number': 302} | {'precision': 0.8226950354609929, 'recall': 0.9133858267716536, 'f1': 0.8656716417910448, 'number': 127} | {'precision': 0.6904400606980273, 'recall': 0.7176656151419558, 'f1': 0.7037896365042536, 'number': 634} | {'precision': 0.7746478873239436, 'recall': 0.868421052631579, 'f1': 0.8188585607940446, 'number': 380} | {'precision': 0.8870292887029289, 'recall': 0.9506726457399103, 'f1': 0.9177489177489178, 'number': 223} | {'precision': 0.7567567567567568, 'recall': 0.875, 'f1': 0.8115942028985507, 'number': 32} | {'precision': 0.8695652173913043, 'recall': 0.9523809523809523, 'f1': 0.909090909090909, 'number': 84} | {'precision': 0.6, 'recall': 0.673469387755102, 'f1': 0.6346153846153846, 'number': 49} | |
| | 0.2932 | 3.0 | 771 | 0.1810 | {'precision': 0.8215384615384616, 'recall': 0.8841059602649006, 'f1': 0.8516746411483254, 'number': 302} | {'precision': 0.8041958041958042, 'recall': 0.905511811023622, 'f1': 0.8518518518518519, 'number': 127} | {'precision': 0.6886657101865137, 'recall': 0.7570977917981072, 'f1': 0.7212622088655146, 'number': 634} | {'precision': 0.7944162436548223, 'recall': 0.8236842105263158, 'f1': 0.8087855297157622, 'number': 380} | {'precision': 0.8647540983606558, 'recall': 0.9461883408071748, 'f1': 0.9036402569593148, 'number': 223} | {'precision': 0.65, 'recall': 0.8125, 'f1': 0.7222222222222223, 'number': 32} | {'precision': 0.9285714285714286, 'recall': 0.9285714285714286, 'f1': 0.9285714285714286, 'number': 84} | {'precision': 0.5666666666666667, 'recall': 0.6938775510204082, 'f1': 0.6238532110091742, 'number': 49} | |
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|
| ### Framework versions |
|
|
| - Transformers 4.38.2 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
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|
