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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_lr4
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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_lr4
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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: 0.1801
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- Precision: 0.6659
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- Recall: 0.7266
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- F1: 0.6950
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- Accuracy: 0.9478
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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-08
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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 | 0.2732 | 0.6635 | 0.6619 | 0.6627 | 0.9415 |
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| 0.2772 | 2.0 | 934 | 0.2436 | 0.6562 | 0.6820 | 0.6688 | 0.9426 |
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| 0.2379 | 3.0 | 1401 | 0.2224 | 0.6550 | 0.6980 | 0.6758 | 0.9437 |
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| 0.2142 | 4.0 | 1868 | 0.2071 | 0.6597 | 0.7104 | 0.6841 | 0.9450 |
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| 0.1968 | 5.0 | 2335 | 0.1960 | 0.6597 | 0.7165 | 0.6869 | 0.9461 |
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| 0.1888 | 6.0 | 2802 | 0.1884 | 0.6610 | 0.7195 | 0.6890 | 0.9468 |
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| 0.1788 | 7.0 | 3269 | 0.1835 | 0.6641 | 0.7244 | 0.6929 | 0.9474 |
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| 0.1768 | 8.0 | 3736 | 0.1808 | 0.6652 | 0.7258 | 0.6942 | 0.9477 |
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| 0.1695 | 9.0 | 4203 | 0.1801 | 0.6659 | 0.7266 | 0.6950 | 0.9478 |
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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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