BERT_Mod_2

This model is a fine-tuned version of distilbert-base-cased on the glue dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.5659
• eval_accuracy: 0.9037
• eval_runtime: 0.3838
• eval_samples_per_second: 2271.724
• eval_steps_per_second: 143.285
• epoch: 0.01
• step: 49

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Framework versions

• Transformers 4.20.1
• Pytorch 1.12.0
• Datasets 2.4.0
• Tokenizers 0.12.1