| | --- |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - bc5_cdr |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: electramed-small-BC5CDR-ner |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: bc5_cdr |
| | type: bc5_cdr |
| | config: BC5CDR-Disease |
| | split: train |
| | args: BC5CDR-Disease |
| | metrics: |
| | - name: Precision |
| | type: precision |
| | value: 0.8091945430760605 |
| | - name: Recall |
| | type: recall |
| | value: 0.882862677133245 |
| | - name: F1 |
| | type: f1 |
| | value: 0.8444249427136657 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9685851703406814 |
| | --- |
| | |
| | <!-- 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-BC5CDR-ner |
| |
|
| | This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the bc5_cdr dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1227 |
| | - Precision: 0.8092 |
| | - Recall: 0.8829 |
| | - F1: 0.8444 |
| | - Accuracy: 0.9686 |
| | |
| | ## 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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.7177 | 1.0 | 286 | 0.6902 | 0.0 | 0.0 | 0.0 | 0.8864 | |
| | | 0.1561 | 2.0 | 572 | 0.3210 | 0.7334 | 0.8104 | 0.7700 | 0.9636 | |
| | | 0.2511 | 3.0 | 858 | 0.2064 | 0.7809 | 0.8711 | 0.8236 | 0.9666 | |
| | | 0.0512 | 4.0 | 1144 | 0.1599 | 0.7937 | 0.8751 | 0.8324 | 0.9689 | |
| | | 0.083 | 5.0 | 1430 | 0.1449 | 0.7983 | 0.8804 | 0.8373 | 0.9679 | |
| | | 0.0412 | 6.0 | 1716 | 0.1315 | 0.8141 | 0.8825 | 0.8469 | 0.9701 | |
| | | 0.1437 | 7.0 | 2002 | 0.1258 | 0.8227 | 0.8758 | 0.8485 | 0.9699 | |
| | | 0.1894 | 8.0 | 2288 | 0.1226 | 0.8141 | 0.8833 | 0.8473 | 0.9696 | |
| | | 0.0236 | 9.0 | 2574 | 0.1220 | 0.8160 | 0.8824 | 0.8479 | 0.9694 | |
| | | 0.0602 | 10.0 | 2860 | 0.1227 | 0.8092 | 0.8829 | 0.8444 | 0.9686 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.21.1 |
| | - Pytorch 1.12.1+cu113 |
| | - Datasets 2.4.0 |
| | - Tokenizers 0.12.1 |
| | |
