2d_800

<!-- 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. -->

2d_800

This model is a fine-tuned version of bert-base-uncased on the ComNum dataset. This model used 800 samples as training, 200 as validation, and 1200 as test on three epochs. It achieves the following results on the evaluation set:

• Loss: 0.4172
• Accuracy: 0.745

This model achieves the following results on the test set:

• Loss: 0.4144
• Accuracy: 0.7509

<!-- {'eval_loss': 0.41437938809394836, 'eval_accuracy': 0.7509, 'eval_runtime': 592.6087, 'eval_samples_per_second': 16.875, 'eval_steps_per_second': 2.109} -->

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0