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nominal-groups-recognition-roberta-clinical-wl-es
This model is a fine-tuned version of plncmm/roberta-clinical-wl-es on the bastianchinchon/spanish_nominal_groups_conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2226
- Body Part Precision: 0.7427
- Body Part Recall: 0.7966
- Body Part F1: 0.7687
- Body Part Number: 413
- Disease Precision: 0.7915
- Disease Recall: 0.8174
- Disease F1: 0.8042
- Disease Number: 975
- Family Member Precision: 0.8286
- Family Member Recall: 0.9667
- Family Member F1: 0.8923
- Family Member Number: 30
- Medication Precision: 0.7905
- Medication Recall: 0.8925
- Medication F1: 0.8384
- Medication Number: 93
- Procedure Precision: 0.7105
- Procedure Recall: 0.7814
- Procedure F1: 0.7443
- Procedure Number: 311
- Overall Precision: 0.7666
- Overall Recall: 0.8128
- Overall F1: 0.7890
- Overall Accuracy: 0.9374
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.356 | 1.0 | 1004 | 0.2241 | 0.7283 | 0.7724 | 0.7497 | 413 | 0.7603 | 0.8133 | 0.7859 | 975 | 0.9062 | 0.9667 | 0.9355 | 30 | 0.7547 | 0.8602 | 0.8040 | 93 | 0.6464 | 0.7524 | 0.6954 | 311 | 0.7345 | 0.7986 | 0.7652 | 0.9319 |
| 0.1823 | 2.0 | 2008 | 0.2226 | 0.7427 | 0.7966 | 0.7687 | 413 | 0.7915 | 0.8174 | 0.8042 | 975 | 0.8286 | 0.9667 | 0.8923 | 30 | 0.7905 | 0.8925 | 0.8384 | 93 | 0.7105 | 0.7814 | 0.7443 | 311 | 0.7666 | 0.8128 | 0.7890 | 0.9374 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
