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---
tags:
- generated_from_trainer
datasets:
- nsmc
metrics:
- accuracy
- f1
- recall
- precision
base_model: beomi/kcbert-base
model-index:
- name: kcbert-base-finetuned-nsmc
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: nsmc
type: nsmc
args: default
metrics:
- type: accuracy
value: 0.90198
name: Accuracy
- type: f1
value: 0.9033161705233671
name: F1
- type: recall
value: 0.9095062169785088
name: Recall
- type: precision
value: 0.8972098126812446
name: Precision
---
<!-- 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. -->
# kcbert-base-finetuned-nsmc
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: 0.4197
- Accuracy: 0.9020
- F1: 0.9033
- Recall: 0.9095
- Precision: 0.8972
## 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: 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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.3028 | 0.32 | 3000 | 0.2994 | 0.8769 | 0.8732 | 0.8422 | 0.9066 |
| 0.2833 | 0.64 | 6000 | 0.2766 | 0.8880 | 0.8844 | 0.8512 | 0.9203 |
| 0.2719 | 0.96 | 9000 | 0.2527 | 0.8980 | 0.8981 | 0.8933 | 0.9030 |
| 0.1938 | 1.28 | 12000 | 0.2934 | 0.8969 | 0.8965 | 0.8869 | 0.9062 |
| 0.1907 | 1.6 | 15000 | 0.3141 | 0.8992 | 0.8999 | 0.9003 | 0.8996 |
| 0.1824 | 1.92 | 18000 | 0.3537 | 0.8986 | 0.8964 | 0.8711 | 0.9232 |
| 0.1261 | 2.24 | 21000 | 0.4197 | 0.9020 | 0.9033 | 0.9095 | 0.8972 |
| 0.1237 | 2.56 | 24000 | 0.4170 | 0.8995 | 0.9017 | 0.9156 | 0.8882 |
| 0.1182 | 2.88 | 27000 | 0.4165 | 0.9020 | 0.9036 | 0.9130 | 0.8945 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.14.0
- Tokenizers 0.10.3
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