---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DeBERTa-finetuned-ner-conll2003
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: validation
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9516691579471849
    - name: Recall
      type: recall
      value: 0.9643217771794009
    - name: F1
      type: f1
      value: 0.9579536905458497
    - name: Accuracy
      type: accuracy
      value: 0.9914139088097901
---

<!-- 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-finetuned-ner-conll2003

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0406
- Precision: 0.9517
- Recall: 0.9643
- F1: 0.9580
- Accuracy: 0.9914

## 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: 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0597        | 1.0   | 1756 | 0.0486          | 0.9316    | 0.9493 | 0.9404 | 0.9884   |
| 0.0286        | 2.0   | 3512 | 0.0413          | 0.9479    | 0.9606 | 0.9542 | 0.9908   |
| 0.0173        | 3.0   | 5268 | 0.0406          | 0.9517    | 0.9643 | 0.9580 | 0.9914   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3