mbti_4axis_koelectra

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mbti_4axis_koelectra

This model is a fine-tuned version of monologg/koelectra-base-v3-discriminator on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6614
• Accuracy: 0.6027
• F1: 0.6878

Model description

깃허브에 dev0jeamin이 만든 Korean-MBTI-Conversation-Dataset을 이용해 koelectra를 파인튜닝한 모델입니다. 16가지 mbti값을 4축의 2진분류로 나눈 값을 예측하는 모델로 불균형한 데이터셋을 고려해 불균형한 모델에 가중값이 적용되었으므로 다시 튜닝한다면 이 사항을 고려해야합니다. 무슨이유인지 몰라도 제대로된 결과값이 산출되지 않아 큰 기대를 하지 않는게 좋습니다

Intended uses & limitations

More information needed

Training and evaluation data

• github/dev-jaemin/Korean-MBTI-Conversation-Dataset
• use data in qna_cleaned.tsv, multiple_qna_cleaned.tsv
• refine [answer, a_mbti]
• concat both refiend data
• Training and evaluation data split 8:2 ratio

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 5

Training results

Framework versions

• Transformers 4.55.2
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.21.4