---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model-index:
- name: kobert-finetuned-klue-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: klue
      type: klue
      config: ner
      split: validation
      args: ner
    metrics:
    - name: F1
      type: f1
      value: 0.26395413647583404
---

<!-- 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. -->

# kobert-finetuned-klue-ner

This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4238
- F1: 0.2640

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5975        | 1.0   | 1313 | 0.5314          | 0.1794 |
| 0.4068        | 2.0   | 2626 | 0.4611          | 0.2331 |
| 0.3366        | 3.0   | 3939 | 0.4264          | 0.2598 |
| 0.2933        | 4.0   | 5252 | 0.4238          | 0.2640 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3