| | --- |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - bc2gm_corpus |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: electramed-small-BC2GM-ner |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: bc2gm_corpus |
| | type: bc2gm_corpus |
| | config: bc2gm_corpus |
| | split: train |
| | args: bc2gm_corpus |
| | metrics: |
| | - name: Precision |
| | type: precision |
| | value: 0.7652071701439906 |
| | - name: Recall |
| | type: recall |
| | value: 0.823399209486166 |
| | - name: F1 |
| | type: f1 |
| | value: 0.7932373771989948 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9756735092182762 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # electramed-small-BC2GM-ner |
| |
|
| | This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the bc2gm_corpus dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0720 |
| | - Precision: 0.7652 |
| | - Recall: 0.8234 |
| | - F1: 0.7932 |
| | - Accuracy: 0.9757 |
| | |
| | ## Model description |
| | |
| | More information needed |
| | |
| | ## Intended uses & limitations |
| | |
| | More information needed |
| | |
| | ## Training and evaluation data |
| | |
| | More information needed |
| | |
| | ## Training procedure |
| | |
| | ### Training hyperparameters |
| | |
| | The following hyperparameters were used during training: |
| | - learning_rate: 2e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 10 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.085 | 1.0 | 782 | 0.1112 | 0.6147 | 0.7777 | 0.6867 | 0.9634 | |
| | | 0.0901 | 2.0 | 1564 | 0.0825 | 0.7141 | 0.8028 | 0.7559 | 0.9720 | |
| | | 0.0303 | 3.0 | 2346 | 0.0759 | 0.7310 | 0.8049 | 0.7662 | 0.9724 | |
| | | 0.0037 | 4.0 | 3128 | 0.0735 | 0.7430 | 0.8168 | 0.7781 | 0.9735 | |
| | | 0.0325 | 5.0 | 3910 | 0.0723 | 0.7571 | 0.8142 | 0.7846 | 0.9748 | |
| | | 0.0582 | 6.0 | 4692 | 0.0701 | 0.7664 | 0.8144 | 0.7897 | 0.9759 | |
| | | 0.0073 | 7.0 | 5474 | 0.0701 | 0.7711 | 0.8212 | 0.7953 | 0.9761 | |
| | | 0.1031 | 8.0 | 6256 | 0.0712 | 0.7602 | 0.8258 | 0.7916 | 0.9756 | |
| | | 0.0248 | 9.0 | 7038 | 0.0722 | 0.7691 | 0.8231 | 0.7952 | 0.9759 | |
| | | 0.0136 | 10.0 | 7820 | 0.0720 | 0.7652 | 0.8234 | 0.7932 | 0.9757 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.22.1 |
| | - Pytorch 1.12.1+cu113 |
| | - Datasets 2.4.0 |
| | - Tokenizers 0.12.1 |
| | |
