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--- |
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language: |
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- es |
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license: openrail |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: nominal-groups-recognition-medical-disease-competencia2-bert-medical-ner |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# nominal-groups-recognition-medical-disease-competencia2-bert-medical-ner |
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This model is a fine-tuned version of [ukkendane/bert-medical-ner](https://huggingface.co/ukkendane/bert-medical-ner) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3607 |
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- Body Part Precision: 0.6555 |
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- Body Part Recall: 0.7094 |
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- Body Part F1: 0.6814 |
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- Body Part Number: 413 |
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- Disease Precision: 0.6835 |
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- Disease Recall: 0.7067 |
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- Disease F1: 0.6949 |
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- Disease Number: 975 |
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- Family Member Precision: 1.0 |
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- Family Member Recall: 0.6 |
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- Family Member F1: 0.7500 |
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- Family Member Number: 30 |
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- Medication Precision: 0.7647 |
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- Medication Recall: 0.6989 |
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- Medication F1: 0.7303 |
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- Medication Number: 93 |
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- Procedure Precision: 0.5385 |
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- Procedure Recall: 0.5402 |
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- Procedure F1: 0.5393 |
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- Procedure Number: 311 |
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- Overall Precision: 0.6594 |
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- Overall Recall: 0.6767 |
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- Overall F1: 0.6679 |
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- Overall Accuracy: 0.9079 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 13 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| 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 | |
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|:-------------:|:-----:|:-----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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