transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:nsmc base_model:beomi/kcbert-base base_model:finetune:beomi/kcbert-base license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: beomi/kcbert-base
datasets:
- nsmc
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: kcbert
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: nsmc
      type: nsmc
      config: default
      split: test
      args: default
    metrics:
    - type: accuracy
      value: 0.90152
      name: Accuracy
---

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# kcbert

This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the nsmc dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3059
- Accuracy: 0.9015

## 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-06
- 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: cosine
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2703        | 1.0   | 9375  | 0.2601          | 0.8960   |
| 0.2128        | 2.0   | 18750 | 0.2690          | 0.8997   |
| 0.1637        | 3.0   | 28125 | 0.3059          | 0.9015   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
