---
license: cc-by-sa-4.0
base_model: klue/bert-base
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- accuracy
model-index:
- name: bert-base-finetuned-ynat
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: klue
      type: klue
      config: ynat
      split: validation
      args: ynat
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8659273086636653
---

<!-- 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. -->

# bert-base-finetuned-ynat

This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3691
- Accuracy: 0.8659

## 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: 512
- eval_batch_size: 512
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 90   | 0.4090          | 0.8599   |
| No log        | 2.0   | 180  | 0.3929          | 0.8578   |
| No log        | 3.0   | 270  | 0.3703          | 0.8648   |
| No log        | 4.0   | 360  | 0.3714          | 0.8631   |
| No log        | 5.0   | 450  | 0.3691          | 0.8659   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.14.1