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---
language:
- es
license: openrail
tags:
- generated_from_trainer
model-index:
- name: nominal-groups-recognition-medical-disease-competencia2-bert-medical-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. -->
# nominal-groups-recognition-medical-disease-competencia2-bert-medical-ner
This model is a fine-tuned version of [ukkendane/bert-medical-ner](https://huggingface.co/ukkendane/bert-medical-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3607
- Body Part Precision: 0.6555
- Body Part Recall: 0.7094
- Body Part F1: 0.6814
- Body Part Number: 413
- Disease Precision: 0.6835
- Disease Recall: 0.7067
- Disease F1: 0.6949
- Disease Number: 975
- Family Member Precision: 1.0
- Family Member Recall: 0.6
- Family Member F1: 0.7500
- Family Member Number: 30
- Medication Precision: 0.7647
- Medication Recall: 0.6989
- Medication F1: 0.7303
- Medication Number: 93
- Procedure Precision: 0.5385
- Procedure Recall: 0.5402
- Procedure F1: 0.5393
- Procedure Number: 311
- Overall Precision: 0.6594
- Overall Recall: 0.6767
- Overall F1: 0.6679
- Overall Accuracy: 0.9079
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4541 | 1.0 | 8025 | 0.3607 | 0.6555 | 0.7094 | 0.6814 | 413 | 0.6835 | 0.7067 | 0.6949 | 975 | 1.0 | 0.6 | 0.7500 | 30 | 0.7647 | 0.6989 | 0.7303 | 93 | 0.5385 | 0.5402 | 0.5393 | 311 | 0.6594 | 0.6767 | 0.6679 | 0.9079 |
| 0.3149 | 2.0 | 16050 | 0.3607 | 0.6555 | 0.7094 | 0.6814 | 413 | 0.6835 | 0.7067 | 0.6949 | 975 | 1.0 | 0.6 | 0.7500 | 30 | 0.7647 | 0.6989 | 0.7303 | 93 | 0.5385 | 0.5402 | 0.5393 | 311 | 0.6594 | 0.6767 | 0.6679 | 0.9079 |
| 0.3161 | 3.0 | 24075 | 0.3607 | 0.6555 | 0.7094 | 0.6814 | 413 | 0.6835 | 0.7067 | 0.6949 | 975 | 1.0 | 0.6 | 0.7500 | 30 | 0.7647 | 0.6989 | 0.7303 | 93 | 0.5385 | 0.5402 | 0.5393 | 311 | 0.6594 | 0.6767 | 0.6679 | 0.9079 |
| 0.3181 | 4.0 | 32100 | 0.3607 | 0.6555 | 0.7094 | 0.6814 | 413 | 0.6835 | 0.7067 | 0.6949 | 975 | 1.0 | 0.6 | 0.7500 | 30 | 0.7647 | 0.6989 | 0.7303 | 93 | 0.5385 | 0.5402 | 0.5393 | 311 | 0.6594 | 0.6767 | 0.6679 | 0.9079 |
| 0.3164 | 5.0 | 40125 | 0.3607 | 0.6555 | 0.7094 | 0.6814 | 413 | 0.6835 | 0.7067 | 0.6949 | 975 | 1.0 | 0.6 | 0.7500 | 30 | 0.7647 | 0.6989 | 0.7303 | 93 | 0.5385 | 0.5402 | 0.5393 | 311 | 0.6594 | 0.6767 | 0.6679 | 0.9079 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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