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---
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
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