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A-Funakoshi
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bert-finetuned-multilingual-sentiments-adamw

Text Classification
Transformers
PyTorch
Japanese
bert
Inference Endpoints
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  • ベースモデル：cl-tohoku/bert-base-japanese-whole-word-masking

  • データセット：tyqiangz/multilingual-sentiments

  • バッチサイズ: 16固定

  • オプティマイザ: adamw

  • Optunaでハイパーパラメータ探索

    • 学習率スケジュールのタイプ(lr_scheduler_type): constant, linear, cosine
    • 学習率(learning rate): 1e-6 ~ 1e-4
    • 勾配累積ステップ(gradient_accumulation_steps): 1, 2, 4, 8, 16
    • 正則化(weight_decay): 1e-6 ~ 1e-1

  • Optunaでの探索結果は以下

    • 学習率スケジュールタイプ(lr_scheduler_type): cosine
    • 学習率(learning rate): 2.8200169553241494e-05
    • 勾配累積ステップ(gradient_accumulation_steps): 1
    • 正則化(weight_decay): 0.00017069715801856997
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