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---
language:
- es
tags:
- generated_from_trainer
datasets:
- jpherrerap/competencia2
model-index:
- name: ner-bert-base-spanish-wwm-uncased
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. -->
# ner-bert-base-spanish-wwm-uncased
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the jpherrerap/competencia2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5112
- Body Part Precision: 0.0
- Body Part Recall: 0.0
- Body Part F1: 0.0
- Body Part Number: 0
- Disease Precision: 0.0
- Disease Recall: 0.0
- Disease F1: 0.0
- Disease Number: 0
- Family Member Precision: 0.0
- Family Member Recall: 0.0
- Family Member F1: 0.0
- Family Member Number: 0
- Medication Precision: 0.0
- Medication Recall: 0.0
- Medication F1: 0.0
- Medication Number: 0
- Procedure Precision: 0.0
- Procedure Recall: 0.0
- Procedure F1: 0.0
- Procedure Number: 0
- Overall Precision: 0.0
- Overall Recall: 0.0
- Overall F1: 0.0
- Overall Accuracy: 0.6713
## 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: 5e-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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3372 | 1.0 | 1004 | 1.5112 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0.6713 |
| 0.1611 | 2.0 | 2008 | 1.7235 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0.6705 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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