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COMPner-bert-base-spanish-wwm-cased
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the simonestradasch/NERcomp dataset. It achieves the following results on the evaluation set:
- Loss: 0.2793
- Body Part Precision: 0.6700
- Body Part Recall: 0.7186
- Body Part F1: 0.6934
- Body Part Number: 565
- Disease Precision: 0.6966
- Disease Recall: 0.7533
- Disease F1: 0.7238
- Disease Number: 1350
- Family Member Precision: 0.9
- Family Member Recall: 0.75
- Family Member F1: 0.8182
- Family Member Number: 24
- Medication Precision: 0.7143
- Medication Recall: 0.6190
- Medication F1: 0.6633
- Medication Number: 105
- Procedure Precision: 0.5233
- Procedure Recall: 0.5125
- Procedure F1: 0.5178
- Procedure Number: 439
- Overall Precision: 0.6640
- Overall Recall: 0.6971
- Overall F1: 0.6802
- Overall Accuracy: 0.9136
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 13
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Body Part Precision | Body Part Recall | Body Part F1 | Body Part Number | Disease Precision | Disease Recall | Disease F1 | Disease Number | Family Member Precision | Family Member Recall | Family Member F1 | Family Member Number | Medication Precision | Medication Recall | Medication F1 | Medication Number | Procedure Precision | Procedure Recall | Procedure F1 | Procedure Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4741 | 1.0 | 703 | 0.2932 | 0.6449 | 0.6301 | 0.6374 | 565 | 0.6984 | 0.7170 | 0.7076 | 1350 | 0.9412 | 0.6667 | 0.7805 | 24 | 0.8551 | 0.5619 | 0.6782 | 105 | 0.5113 | 0.3599 | 0.4225 | 439 | 0.6674 | 0.6271 | 0.6466 | 0.9091 |
| 0.259 | 2.0 | 1406 | 0.2793 | 0.6700 | 0.7186 | 0.6934 | 565 | 0.6966 | 0.7533 | 0.7238 | 1350 | 0.9 | 0.75 | 0.8182 | 24 | 0.7143 | 0.6190 | 0.6633 | 105 | 0.5233 | 0.5125 | 0.5178 | 439 | 0.6640 | 0.6971 | 0.6802 | 0.9136 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
