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

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

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	100	0.6870	0.54
No log	2.0	200	0.6131	0.65
No log	3.0	300	0.4172	0.745

Framework versions

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