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--- |
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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: electramed-small-ADE-ner |
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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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# electramed-small-ADE-ner |
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This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1548 |
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- Precision: 0.8358 |
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- Recall: 0.9064 |
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- F1: 0.8697 |
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- Accuracy: 0.9581 |
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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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| 0.5587 | 1.0 | 201 | 0.4107 | 0.7291 | 0.7982 | 0.7621 | 0.8983 | |
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| 0.2114 | 2.0 | 402 | 0.2663 | 0.7716 | 0.8826 | 0.8234 | 0.9445 | |
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| 0.1421 | 3.0 | 603 | 0.2183 | 0.8033 | 0.9030 | 0.8502 | 0.9488 | |
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| 0.2204 | 4.0 | 804 | 0.1878 | 0.8279 | 0.9012 | 0.8630 | 0.9553 | |
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| 0.5825 | 5.0 | 1005 | 0.1712 | 0.8289 | 0.8967 | 0.8615 | 0.9566 | |
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| 0.0685 | 6.0 | 1206 | 0.1647 | 0.8333 | 0.9067 | 0.8685 | 0.9572 | |
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| 0.0973 | 7.0 | 1407 | 0.1593 | 0.8365 | 0.9049 | 0.8693 | 0.9578 | |
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| 0.1683 | 8.0 | 1608 | 0.1574 | 0.8367 | 0.9082 | 0.8710 | 0.9577 | |
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| 0.065 | 9.0 | 1809 | 0.1557 | 0.8397 | 0.9052 | 0.8712 | 0.9583 | |
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| 0.179 | 10.0 | 2010 | 0.1548 | 0.8358 | 0.9064 | 0.8697 | 0.9581 | |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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