<!-- 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. -->
nercomp3-bert-base-spanish-wwm-uncased
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on the simonestradasch/NERcomp2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3191
- Body Part Precision: 0.6521
- Body Part Recall: 0.7215
- Body Part F1: 0.6851
- Body Part Number: 413
- Disease Precision: 0.6954
- Disease Recall: 0.7190
- Disease F1: 0.7070
- Disease Number: 975
- Family Member Precision: 0.8966
- Family Member Recall: 0.8667
- Family Member F1: 0.8814
- Family Member Number: 30
- Medication Precision: 0.75
- Medication Recall: 0.7742
- Medication F1: 0.7619
- Medication Number: 93
- Procedure Precision: 0.5505
- Procedure Recall: 0.5434
- Procedure F1: 0.5469
- Procedure Number: 311
- Overall Precision: 0.6674
- Overall Recall: 0.6948
- Overall F1: 0.6808
- Overall Accuracy: 0.9111
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3658 | 1.0 | 4013 | 0.3191 | 0.6521 | 0.7215 | 0.6851 | 413 | 0.6954 | 0.7190 | 0.7070 | 975 | 0.8966 | 0.8667 | 0.8814 | 30 | 0.75 | 0.7742 | 0.7619 | 93 | 0.5505 | 0.5434 | 0.5469 | 311 | 0.6674 | 0.6948 | 0.6808 | 0.9111 |
| 0.2696 | 2.0 | 8026 | 0.3191 | 0.6521 | 0.7215 | 0.6851 | 413 | 0.6954 | 0.7190 | 0.7070 | 975 | 0.8966 | 0.8667 | 0.8814 | 30 | 0.75 | 0.7742 | 0.7619 | 93 | 0.5505 | 0.5434 | 0.5469 | 311 | 0.6674 | 0.6948 | 0.6808 | 0.9111 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
