---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: microsoft/deberta-base
model-index:
- name: deberta-finetuned-ner
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: conll2003
      type: conll2003
      args: conll2003
    metrics:
    - type: precision
      value: 0.9577488309953239
      name: Precision
    - type: recall
      value: 0.9651632446987546
      name: Recall
    - type: f1
      value: 0.961441743503772
      name: F1
    - type: accuracy
      value: 0.9907182964622135
      name: Accuracy
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: test
    metrics:
    - type: accuracy
      value: 0.9108823919384779
      name: Accuracy
      verified: true
    - type: precision
      value: 0.9308372971460548
      name: Precision
      verified: true
    - type: recall
      value: 0.9213792387183881
      name: Recall
      verified: true
    - type: f1
      value: 0.9260841198729938
      name: F1
      verified: true
    - type: loss
      value: 0.8661637306213379
      name: loss
      verified: true
---

<!-- 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

This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0515
- Precision: 0.9577
- Recall: 0.9652
- F1: 0.9614
- Accuracy: 0.9907

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0742        | 1.0   | 1756 | 0.0526          | 0.9390    | 0.9510 | 0.9450 | 0.9868   |
| 0.0374        | 2.0   | 3512 | 0.0528          | 0.9421    | 0.9554 | 0.9487 | 0.9879   |
| 0.0205        | 3.0   | 5268 | 0.0505          | 0.9505    | 0.9636 | 0.9570 | 0.9900   |
| 0.0089        | 4.0   | 7024 | 0.0528          | 0.9531    | 0.9636 | 0.9583 | 0.9898   |
| 0.0076        | 5.0   | 8780 | 0.0515          | 0.9577    | 0.9652 | 0.9614 | 0.9907   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1