| | --- |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - jnlpba |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: electramed-small-JNLPBA-ner |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: jnlpba |
| | type: jnlpba |
| | config: jnlpba |
| | split: train |
| | args: jnlpba |
| | metrics: |
| | - name: Precision |
| | type: precision |
| | value: 0.8224512128396863 |
| | - name: Recall |
| | type: recall |
| | value: 0.878188899707887 |
| | - name: F1 |
| | type: f1 |
| | value: 0.8494066679223958 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9620705451213926 |
| | --- |
| | |
| | <!-- 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-JNLPBA-ner |
| |
|
| | This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the jnlpba dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1167 |
| | - Precision: 0.8225 |
| | - Recall: 0.8782 |
| | - F1: 0.8494 |
| | - Accuracy: 0.9621 |
| |
|
| | ## 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 | |
| | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.398 | 1.0 | 2087 | 0.1941 | 0.7289 | 0.7936 | 0.7599 | 0.9441 | |
| | | 0.0771 | 2.0 | 4174 | 0.1542 | 0.7734 | 0.8348 | 0.8029 | 0.9514 | |
| | | 0.1321 | 3.0 | 6261 | 0.1413 | 0.7890 | 0.8492 | 0.8180 | 0.9546 | |
| | | 0.2302 | 4.0 | 8348 | 0.1326 | 0.8006 | 0.8589 | 0.8287 | 0.9562 | |
| | | 0.0723 | 5.0 | 10435 | 0.1290 | 0.7997 | 0.8715 | 0.8340 | 0.9574 | |
| | | 0.171 | 6.0 | 12522 | 0.1246 | 0.8115 | 0.8722 | 0.8408 | 0.9593 | |
| | | 0.1058 | 7.0 | 14609 | 0.1204 | 0.8148 | 0.8757 | 0.8441 | 0.9604 | |
| | | 0.1974 | 8.0 | 16696 | 0.1178 | 0.8181 | 0.8779 | 0.8470 | 0.9614 | |
| | | 0.0663 | 9.0 | 18783 | 0.1168 | 0.8239 | 0.8781 | 0.8501 | 0.9620 | |
| | | 0.1022 | 10.0 | 20870 | 0.1167 | 0.8225 | 0.8782 | 0.8494 | 0.9621 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.21.1 |
| | - Pytorch 1.12.1+cu113 |
| | - Datasets 2.4.0 |
| | - Tokenizers 0.12.1 |
| |
|
