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README.md
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---
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tags:
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- generated_from_trainer
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datasets:
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- i2b22014
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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: electramed-small-deid2014-ner-v4
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results:
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- task:
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name: Token Classification
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type: token-classification
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dataset:
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name: i2b22014
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type: i2b22014
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config: i2b22014-deid
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split: train
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args: i2b22014-deid
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metrics:
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- name: Precision
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type: precision
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value: 0.7571112095702259
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- name: Recall
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type: recall
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value: 0.7853663020498207
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- name: F1
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type: f1
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value: 0.770979967514889
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- name: Accuracy
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type: accuracy
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value: 0.9906153616114308
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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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# electramed-small-deid2014-ner-v4
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This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the i2b22014 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0362
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- Precision: 0.7571
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- Recall: 0.7854
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- F1: 0.7710
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- Accuracy: 0.9906
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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: 16
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- eval_batch_size: 16
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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: 10
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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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| 0.0143 | 1.0 | 1838 | 0.1451 | 0.3136 | 0.3463 | 0.3291 | 0.9700 |
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| 0.0033 | 2.0 | 3676 | 0.0940 | 0.4293 | 0.4861 | 0.4559 | 0.9758 |
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| 0.0014 | 3.0 | 5514 | 0.0725 | 0.4906 | 0.5766 | 0.5301 | 0.9799 |
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| 0.0007 | 4.0 | 7352 | 0.0568 | 0.6824 | 0.7022 | 0.6921 | 0.9860 |
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| 0.0112 | 5.0 | 9190 | 0.0497 | 0.6966 | 0.7400 | 0.7177 | 0.9870 |
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| 0.0002 | 6.0 | 11028 | 0.0442 | 0.7126 | 0.7549 | 0.7332 | 0.9878 |
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| 0.0002 | 7.0 | 12866 | 0.0404 | 0.7581 | 0.7591 | 0.7586 | 0.9896 |
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| 0.0002 | 8.0 | 14704 | 0.0376 | 0.7540 | 0.7804 | 0.7670 | 0.9904 |
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| 0.0002 | 9.0 | 16542 | 0.0367 | 0.7548 | 0.7825 | 0.7684 | 0.9905 |
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| 0.0001 | 10.0 | 18380 | 0.0362 | 0.7571 | 0.7854 | 0.7710 | 0.9906 |
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### Framework versions
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- Transformers 4.22.1
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- Pytorch 1.12.1+cu113
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- Datasets 2.5.1
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- Tokenizers 0.12.1
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