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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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- ade_drug_effect_ner |
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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-DRUG-EFFECT-ner |
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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: ade_drug_effect_ner |
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type: ade_drug_effect_ner |
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config: ade |
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split: train |
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args: ade |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7745054945054946 |
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- name: Recall |
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type: recall |
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value: 0.6555059523809523 |
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- name: F1 |
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type: f1 |
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value: 0.7100544025790851 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9310355073540336 |
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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-DRUG-EFFECT-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 the ade_drug_effect_ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1630 |
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- Precision: 0.7745 |
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- Recall: 0.6555 |
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- F1: 0.7101 |
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- Accuracy: 0.9310 |
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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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| 0.4498 | 1.0 | 336 | 0.3042 | 0.5423 | 0.6295 | 0.5826 | 0.9114 | |
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| 0.2572 | 2.0 | 672 | 0.2146 | 0.7596 | 0.6194 | 0.6824 | 0.9276 | |
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| 0.1542 | 3.0 | 1008 | 0.1894 | 0.7806 | 0.6168 | 0.6891 | 0.9299 | |
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| 0.1525 | 4.0 | 1344 | 0.1771 | 0.7832 | 0.625 | 0.6952 | 0.9309 | |
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| 0.1871 | 5.0 | 1680 | 0.1723 | 0.7271 | 0.6920 | 0.7091 | 0.9304 | |
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| 0.1425 | 6.0 | 2016 | 0.1683 | 0.7300 | 0.6979 | 0.7136 | 0.9297 | |
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| 0.1638 | 7.0 | 2352 | 0.1654 | 0.7432 | 0.6771 | 0.7086 | 0.9306 | |
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| 0.1592 | 8.0 | 2688 | 0.1635 | 0.7613 | 0.6585 | 0.7062 | 0.9305 | |
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| 0.1882 | 9.0 | 3024 | 0.1625 | 0.7858 | 0.6373 | 0.7038 | 0.9309 | |
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| 0.1339 | 10.0 | 3360 | 0.1630 | 0.7745 | 0.6555 | 0.7101 | 0.9310 | |
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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 2.4.0 |
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- Tokenizers 0.12.1 |
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