---
base_model: klue/roberta-base
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: klue_ner_roberta_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: klue
      type: klue
      config: ner
      split: validation
      args: ner
    metrics:
    - name: Precision
      type: precision
      value: 0.7949828178694158
    - name: Recall
      type: recall
      value: 0.8113207547169812
    - name: F1
      type: f1
      value: 0.8030686985802062
    - name: Accuracy
      type: accuracy
      value: 0.9595964075839893
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# klue_ner_roberta_model

This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1434
- Precision: 0.7950
- Recall: 0.8113
- F1: 0.8031
- Accuracy: 0.9596

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1526        | 1.0   | 2626 | 0.1732          | 0.7105    | 0.7480 | 0.7288 | 0.9450   |
| 0.1019        | 2.0   | 5252 | 0.1395          | 0.7717    | 0.7894 | 0.7804 | 0.9566   |
| 0.0728        | 3.0   | 7878 | 0.1434          | 0.7950    | 0.8113 | 0.8031 | 0.9596   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3