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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: BERT_ep9_lr1_v1
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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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# BERT_ep9_lr1_v1
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This model is a fine-tuned version of [ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT](https://huggingface.co/ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1282
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- Precision: 0.8707
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- Recall: 0.8828
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- F1: 0.8767
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- Accuracy: 0.9792
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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: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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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: 9
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| No log | 1.0 | 467 | 0.0775 | 0.8385 | 0.8618 | 0.8500 | 0.9759 |
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| 0.1072 | 2.0 | 934 | 0.0781 | 0.8293 | 0.8868 | 0.8571 | 0.9770 |
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| 0.0567 | 3.0 | 1401 | 0.0778 | 0.8604 | 0.8787 | 0.8694 | 0.9781 |
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| 0.0356 | 4.0 | 1868 | 0.0868 | 0.8700 | 0.8555 | 0.8627 | 0.9770 |
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| 0.0214 | 5.0 | 2335 | 0.1146 | 0.8525 | 0.8885 | 0.8701 | 0.9767 |
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| 0.0134 | 6.0 | 2802 | 0.1106 | 0.8690 | 0.8806 | 0.8747 | 0.9786 |
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| 0.0078 | 7.0 | 3269 | 0.1112 | 0.8660 | 0.8863 | 0.8760 | 0.9794 |
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| 0.0051 | 8.0 | 3736 | 0.1251 | 0.8685 | 0.8841 | 0.8762 | 0.9790 |
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| 0.0028 | 9.0 | 4203 | 0.1282 | 0.8707 | 0.8828 | 0.8767 | 0.9792 |
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### Framework versions
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- Transformers 4.27.4
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- Pytorch 2.0.0+cu118
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- Datasets 2.11.0
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- Tokenizers 0.13.3
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