bert-term-importance-v1

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

• Loss: 0.0024
• Mse: 0.0024
• Rmse: 0.0487

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Mse	Rmse
0.0034	1.0	675	0.0036	0.0037	0.0605
0.0028	2.0	1350	0.0028	0.0028	0.0532
0.0036	3.0	2025	0.0028	0.0028	0.0530

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu121
• Datasets 3.5.1
• Tokenizers 0.21.0