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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_dosage_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-DOSAGE-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_dosage_ner |
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type: ade_drug_dosage_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: 0.0 |
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- name: Recall |
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type: recall |
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value: 0.0 |
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- name: F1 |
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type: f1 |
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value: 0.0 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8697318007662835 |
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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-DOSAGE-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_dosage_ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6064 |
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- Precision: 0.0 |
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- Recall: 0.0 |
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- F1: 0.0 |
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- Accuracy: 0.8697 |
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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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| 1.4165 | 1.0 | 14 | 1.3965 | 0.0255 | 0.0636 | 0.0365 | 0.7471 | |
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| 1.2063 | 2.0 | 28 | 1.1702 | 0.0 | 0.0 | 0.0 | 0.8697 | |
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| 0.9527 | 3.0 | 42 | 0.9342 | 0.0 | 0.0 | 0.0 | 0.8697 | |
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| 0.8238 | 4.0 | 56 | 0.7775 | 0.0 | 0.0 | 0.0 | 0.8697 | |
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| 0.7452 | 5.0 | 70 | 0.6945 | 0.0 | 0.0 | 0.0 | 0.8697 | |
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| 0.6386 | 6.0 | 84 | 0.6519 | 0.0 | 0.0 | 0.0 | 0.8697 | |
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| 0.6742 | 7.0 | 98 | 0.6294 | 0.0 | 0.0 | 0.0 | 0.8697 | |
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| 0.6669 | 8.0 | 112 | 0.6162 | 0.0 | 0.0 | 0.0 | 0.8697 | |
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| 0.6595 | 9.0 | 126 | 0.6090 | 0.0 | 0.0 | 0.0 | 0.8697 | |
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| 0.6122 | 10.0 | 140 | 0.6064 | 0.0 | 0.0 | 0.0 | 0.8697 | |
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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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