| | --- |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - bc4chemd |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: electramed-small-BC4CHEMD-ner |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: bc4chemd |
| | type: bc4chemd |
| | config: bc4chemd |
| | split: train |
| | args: bc4chemd |
| | metrics: |
| | - name: Precision |
| | type: precision |
| | value: 0.7715624436835465 |
| | - name: Recall |
| | type: recall |
| | value: 0.6760888102832959 |
| | - name: F1 |
| | type: f1 |
| | value: 0.7206773498518718 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9770623458780496 |
| | --- |
| | |
| | <!-- 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-BC4CHEMD-ner |
| |
|
| | This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the bc4chemd dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0655 |
| | - Precision: 0.7716 |
| | - Recall: 0.6761 |
| | - F1: 0.7207 |
| | - Accuracy: 0.9771 |
| |
|
| | ## 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 | |
| | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.0882 | 1.0 | 1918 | 0.1058 | 0.6615 | 0.3942 | 0.4940 | 0.9635 | |
| | | 0.0555 | 2.0 | 3836 | 0.0820 | 0.7085 | 0.5133 | 0.5954 | 0.9689 | |
| | | 0.0631 | 3.0 | 5754 | 0.0769 | 0.6892 | 0.5743 | 0.6266 | 0.9699 | |
| | | 0.0907 | 4.0 | 7672 | 0.0682 | 0.7623 | 0.5923 | 0.6666 | 0.9740 | |
| | | 0.0313 | 5.0 | 9590 | 0.0675 | 0.7643 | 0.6223 | 0.6860 | 0.9749 | |
| | | 0.0306 | 6.0 | 11508 | 0.0662 | 0.7654 | 0.6398 | 0.6970 | 0.9754 | |
| | | 0.0292 | 7.0 | 13426 | 0.0656 | 0.7694 | 0.6552 | 0.7077 | 0.9763 | |
| | | 0.1025 | 8.0 | 15344 | 0.0658 | 0.7742 | 0.6687 | 0.7176 | 0.9769 | |
| | | 0.0394 | 9.0 | 17262 | 0.0662 | 0.7741 | 0.6731 | 0.7201 | 0.9770 | |
| | | 0.0378 | 10.0 | 19180 | 0.0655 | 0.7716 | 0.6761 | 0.7207 | 0.9771 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.21.1 |
| | - Pytorch 1.12.1+cu113 |
| | - Datasets 2.4.0 |
| | - Tokenizers 0.12.1 |
| |
|
