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README.md
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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_lr2
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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_lr2
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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: 0.0899
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- Precision: 0.8601
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- Recall: 0.8819
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- F1: 0.8709
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- Accuracy: 0.9780
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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-06
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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 | 0.0847 | 0.8136 | 0.8571 | 0.8348 | 0.9722 |
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| 0.1137 | 2.0 | 934 | 0.0748 | 0.8367 | 0.8735 | 0.8547 | 0.9755 |
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| 0.0747 | 3.0 | 1401 | 0.0747 | 0.8550 | 0.8702 | 0.8625 | 0.9769 |
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| 0.0603 | 4.0 | 1868 | 0.0805 | 0.8485 | 0.8765 | 0.8622 | 0.9769 |
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| 0.0479 | 5.0 | 2335 | 0.0830 | 0.8607 | 0.8778 | 0.8692 | 0.9776 |
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| 0.0433 | 6.0 | 2802 | 0.0853 | 0.8560 | 0.8803 | 0.8680 | 0.9775 |
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| 0.0352 | 7.0 | 3269 | 0.0869 | 0.8567 | 0.8852 | 0.8707 | 0.9778 |
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| 0.0329 | 8.0 | 3736 | 0.0884 | 0.8583 | 0.8822 | 0.8701 | 0.9779 |
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| 0.0305 | 9.0 | 4203 | 0.0899 | 0.8601 | 0.8819 | 0.8709 | 0.9780 |
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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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