---
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