roberta-large-finetuned-winogrande

This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.6939
• eval_accuracy: 0.5036
• eval_runtime: 392.378
• eval_samples_per_second: 3.229
• eval_steps_per_second: 0.808
• epoch: 1.01
• step: 1169

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: 1e-05
• train_batch_size: 8
• eval_batch_size: 4
• seed: 11262
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 4000
• num_epochs: 8

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1+cpu
• Datasets 2.10.1
• Tokenizers 0.13.2