Gpt2 Japanese Dutch Synsem

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0004
• train_batch_size: 64
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 256
• total_eval_batch_size: 32
• 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_steps: 1000
• training_steps: 6103
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.57.3
• Pytorch 2.9.1+cu128
• Datasets 3.6.0
• Tokenizers 0.22.1

--- library_name: transformers base_model: zhuojing-huang/gpt2-japanese-20k tags: - generated_from_trainer model-index: - name: gpt2-japanese-dutch-synsem results: [] --- <!-- 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. --> # gpt2-japanese-dutch-synsem This model is a fine-tuned version of [zhuojing-huang/gpt2-japanese-20k](https://huggingface.co/zhuojing-huang/gpt2-japanese-20k) on the None dataset. ## 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: 0.0004 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 256 - total_eval_batch_size: 32 - 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_steps: 1000 - training_steps: 6103 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.57.3 - Pytorch 2.9.1+cu128 - Datasets 3.6.0 - Tokenizers 0.22.1