training_bert_model

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

• Loss: 1.0866
• Accuracy: 0.4318

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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	11	1.1001	0.3182
No log	2.0	22	1.0924	0.3864
No log	3.0	33	1.0881	0.4091
No log	4.0	44	1.0866	0.4318

Framework versions

• Transformers 4.33.1
• Pytorch 2.0.1+cpu
• Datasets 2.14.5
• Tokenizers 0.11.0