---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-base-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9563020492186769
    - name: Recall
      type: recall
      value: 0.9652436720816018
    - name: F1
      type: f1
      value: 0.9607520564042303
    - name: Accuracy
      type: accuracy
      value: 0.9899205302077261
---

<!-- 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-base-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: 0.0501
- Precision: 0.9563
- Recall: 0.9652
- F1: 0.9608
- Accuracy: 0.9899

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1419        | 1.0   | 878  | 0.0628          | 0.9290    | 0.9288 | 0.9289 | 0.9835   |
| 0.0379        | 2.0   | 1756 | 0.0466          | 0.9456    | 0.9567 | 0.9511 | 0.9878   |
| 0.0176        | 3.0   | 2634 | 0.0473          | 0.9539    | 0.9575 | 0.9557 | 0.9890   |
| 0.0098        | 4.0   | 3512 | 0.0468          | 0.9570    | 0.9635 | 0.9603 | 0.9896   |
| 0.0043        | 5.0   | 4390 | 0.0501          | 0.9563    | 0.9652 | 0.9608 | 0.9899   |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3