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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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- bc2gm_corpus |
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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-BC2GM-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: bc2gm_corpus |
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type: bc2gm_corpus |
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config: bc2gm_corpus |
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split: train |
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args: bc2gm_corpus |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7652071701439906 |
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- name: Recall |
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type: recall |
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value: 0.823399209486166 |
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- name: F1 |
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type: f1 |
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value: 0.7932373771989948 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9756735092182762 |
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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-BC2GM-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 bc2gm_corpus dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0720 |
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- Precision: 0.7652 |
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- Recall: 0.8234 |
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- F1: 0.7932 |
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- Accuracy: 0.9757 |
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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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| 0.085 | 1.0 | 782 | 0.1112 | 0.6147 | 0.7777 | 0.6867 | 0.9634 | |
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| 0.0901 | 2.0 | 1564 | 0.0825 | 0.7141 | 0.8028 | 0.7559 | 0.9720 | |
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| 0.0303 | 3.0 | 2346 | 0.0759 | 0.7310 | 0.8049 | 0.7662 | 0.9724 | |
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| 0.0037 | 4.0 | 3128 | 0.0735 | 0.7430 | 0.8168 | 0.7781 | 0.9735 | |
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| 0.0325 | 5.0 | 3910 | 0.0723 | 0.7571 | 0.8142 | 0.7846 | 0.9748 | |
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| 0.0582 | 6.0 | 4692 | 0.0701 | 0.7664 | 0.8144 | 0.7897 | 0.9759 | |
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| 0.0073 | 7.0 | 5474 | 0.0701 | 0.7711 | 0.8212 | 0.7953 | 0.9761 | |
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| 0.1031 | 8.0 | 6256 | 0.0712 | 0.7602 | 0.8258 | 0.7916 | 0.9756 | |
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| 0.0248 | 9.0 | 7038 | 0.0722 | 0.7691 | 0.8231 | 0.7952 | 0.9759 | |
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| 0.0136 | 10.0 | 7820 | 0.0720 | 0.7652 | 0.8234 | 0.7932 | 0.9757 | |
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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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