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: 3e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 10
- total_train_batch_size: 320
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.05
- training_steps: 100
Training results
Framework versions
- Transformers 5.6.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
Corrected held-out evaluation
The saved 100-update model was evaluated on all 9,522 unique held-out examples. Any distributed sampler padding is verified against input IDs and labels and excluded from accuracy and loss. Scores measure the first supervised Yes/No answer token. This evaluation used 8 GPUs; the training hyperparameters above describe the original 16-GPU run.