| | --- |
| | license: mit |
| | base_model: microsoft/deberta-v3-base |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - maccrobat_biomedical_ner |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: deberta-v3-base-finetuned-ner |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: maccrobat_biomedical_ner |
| | type: maccrobat_biomedical_ner |
| | config: default |
| | split: train |
| | args: default |
| | metrics: |
| | - name: Precision |
| | type: precision |
| | value: 0.7843711467324291 |
| | - name: Recall |
| | type: recall |
| | value: 0.7816003686069728 |
| | - name: F1 |
| | type: f1 |
| | value: 0.7829833064081853 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.8584199081903842 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # deberta-v3-base-finetuned-ner |
| |
|
| | This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the maccrobat_biomedical_ner dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.9704 |
| | - Precision: 0.7844 |
| | - Recall: 0.7816 |
| | - F1: 0.7830 |
| | - Accuracy: 0.8584 |
| |
|
| | ## 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: 4.555607052152088e-05 |
| | - train_batch_size: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 30 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | No log | 1.0 | 20 | 0.9499 | 0.7670 | 0.7685 | 0.7678 | 0.8477 | |
| | | No log | 2.0 | 40 | 0.9042 | 0.7721 | 0.7629 | 0.7675 | 0.8484 | |
| | | No log | 3.0 | 60 | 0.9360 | 0.7674 | 0.7573 | 0.7623 | 0.8475 | |
| | | No log | 4.0 | 80 | 0.8984 | 0.7630 | 0.7589 | 0.7609 | 0.8442 | |
| | | No log | 5.0 | 100 | 0.8159 | 0.7695 | 0.7701 | 0.7698 | 0.8495 | |
| | | No log | 6.0 | 120 | 0.8086 | 0.7557 | 0.7730 | 0.7643 | 0.8454 | |
| | | No log | 7.0 | 140 | 0.7937 | 0.7766 | 0.7712 | 0.7739 | 0.8509 | |
| | | No log | 8.0 | 160 | 0.8430 | 0.7703 | 0.7707 | 0.7705 | 0.8513 | |
| | | No log | 9.0 | 180 | 0.8711 | 0.7715 | 0.7710 | 0.7712 | 0.8517 | |
| | | No log | 10.0 | 200 | 0.8649 | 0.7687 | 0.7626 | 0.7656 | 0.8485 | |
| | | No log | 11.0 | 220 | 0.8686 | 0.7817 | 0.7635 | 0.7725 | 0.8516 | |
| | | No log | 12.0 | 240 | 0.8644 | 0.7765 | 0.7802 | 0.7784 | 0.8546 | |
| | | No log | 13.0 | 260 | 0.8680 | 0.7771 | 0.7796 | 0.7783 | 0.8550 | |
| | | No log | 14.0 | 280 | 0.8845 | 0.7728 | 0.7748 | 0.7738 | 0.8528 | |
| | | No log | 15.0 | 300 | 0.9084 | 0.7774 | 0.7713 | 0.7743 | 0.8537 | |
| | | No log | 16.0 | 320 | 0.9396 | 0.7782 | 0.7659 | 0.7720 | 0.8509 | |
| | | No log | 17.0 | 340 | 0.9338 | 0.7776 | 0.7781 | 0.7778 | 0.8547 | |
| | | No log | 18.0 | 360 | 0.9205 | 0.7749 | 0.7770 | 0.7759 | 0.8537 | |
| | | No log | 19.0 | 380 | 0.9426 | 0.7781 | 0.7724 | 0.7752 | 0.8523 | |
| | | No log | 20.0 | 400 | 0.9403 | 0.7769 | 0.7827 | 0.7798 | 0.8550 | |
| | | No log | 21.0 | 420 | 0.9393 | 0.7795 | 0.7713 | 0.7754 | 0.8536 | |
| | | No log | 22.0 | 440 | 0.9618 | 0.7771 | 0.7790 | 0.7780 | 0.8547 | |
| | | No log | 23.0 | 460 | 0.9420 | 0.7814 | 0.7836 | 0.7825 | 0.8582 | |
| | | No log | 24.0 | 480 | 0.9455 | 0.7842 | 0.7808 | 0.7825 | 0.8583 | |
| | | 0.0412 | 25.0 | 500 | 0.9599 | 0.7821 | 0.7801 | 0.7811 | 0.8571 | |
| | | 0.0412 | 26.0 | 520 | 0.9518 | 0.7815 | 0.7833 | 0.7824 | 0.8578 | |
| | | 0.0412 | 27.0 | 540 | 0.9570 | 0.7800 | 0.7818 | 0.7809 | 0.8567 | |
| | | 0.0412 | 28.0 | 560 | 0.9634 | 0.7819 | 0.7801 | 0.7810 | 0.8573 | |
| | | 0.0412 | 29.0 | 580 | 0.9685 | 0.7818 | 0.7831 | 0.7825 | 0.8579 | |
| | | 0.0412 | 30.0 | 600 | 0.9704 | 0.7844 | 0.7816 | 0.7830 | 0.8584 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.39.3 |
| | - Pytorch 2.2.1 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.15.2 |
| |
|
