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nominal-groups-recognition-bert-base-spanish-wwm-cased
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the ALazcanoG/spanish_nominal_groups_conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3362
- Body Part Precision: 0.6830
- Body Part Recall: 0.7409
- Body Part F1: 0.7108
- Body Part Number: 413
- Disease Precision: 0.7439
- Disease Recall: 0.7446
- Disease F1: 0.7442
- Disease Number: 975
- Family Member Precision: 0.7941
- Family Member Recall: 0.9
- Family Member F1: 0.8438
- Family Member Number: 30
- Medication Precision: 0.8734
- Medication Recall: 0.7419
- Medication F1: 0.8023
- Medication Number: 93
- Procedure Precision: 0.6190
- Procedure Recall: 0.6270
- Procedure F1: 0.6230
- Procedure Number: 311
- Overall Precision: 0.7144
- Overall Recall: 0.7261
- Overall F1: 0.7202
- Overall Accuracy: 0.9175
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: 5
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4335 | 1.0 | 1004 | 0.3011 | 0.5944 | 0.7167 | 0.6498 | 413 | 0.7014 | 0.7036 | 0.7025 | 975 | 0.8 | 0.8 | 0.8000 | 30 | 0.7875 | 0.6774 | 0.7283 | 93 | 0.6007 | 0.5177 | 0.5561 | 311 | 0.6634 | 0.6751 | 0.6692 | 0.9063 |
| 0.2379 | 2.0 | 2008 | 0.2920 | 0.6995 | 0.7215 | 0.7104 | 413 | 0.7655 | 0.7097 | 0.7366 | 975 | 0.75 | 0.8 | 0.7742 | 30 | 0.7667 | 0.7419 | 0.7541 | 93 | 0.6094 | 0.6270 | 0.6181 | 311 | 0.7212 | 0.7014 | 0.7112 | 0.9140 |
| 0.1629 | 3.0 | 3012 | 0.3022 | 0.6674 | 0.7530 | 0.7076 | 413 | 0.7286 | 0.7241 | 0.7263 | 975 | 0.8571 | 0.8 | 0.8276 | 30 | 0.8519 | 0.7419 | 0.7931 | 93 | 0.5994 | 0.6495 | 0.6235 | 311 | 0.6975 | 0.7201 | 0.7086 | 0.9170 |
| 0.1143 | 4.0 | 4016 | 0.3362 | 0.6830 | 0.7409 | 0.7108 | 413 | 0.7439 | 0.7446 | 0.7442 | 975 | 0.7941 | 0.9 | 0.8438 | 30 | 0.8734 | 0.7419 | 0.8023 | 93 | 0.6190 | 0.6270 | 0.6230 | 311 | 0.7144 | 0.7261 | 0.7202 | 0.9175 |
| 0.0861 | 5.0 | 5020 | 0.3643 | 0.6806 | 0.7482 | 0.7128 | 413 | 0.7428 | 0.7436 | 0.7432 | 975 | 0.8182 | 0.9 | 0.8571 | 30 | 0.8831 | 0.7312 | 0.8000 | 93 | 0.5928 | 0.6367 | 0.6140 | 311 | 0.7081 | 0.7283 | 0.7181 | 0.9163 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
