---
tags:
- generated_from_trainer
datasets:
- nsmc
metrics:
- accuracy
- f1
model-index:
- name: nsmc_roberta_base_model
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: nsmc
      type: nsmc
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.91174
    - name: F1
      type: f1
      value: 0.9117155392338556
---

<!-- 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. -->

# nsmc_roberta_base_model

This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the nsmc dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2570
- Accuracy: 0.9117
- F1: 0.9117

## 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: 64
- eval_batch_size: 64
- 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 | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2501        | 1.0   | 2344 | 0.2306          | 0.9072   | 0.9072 |
| 0.1805        | 2.0   | 4688 | 0.2306          | 0.9112   | 0.9112 |
| 0.1313        | 3.0   | 7032 | 0.2570          | 0.9117   | 0.9117 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3