vi-fin-news

This model is a fine-tuned version of FPTAI/vibert-base-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4509
• Accuracy: 0.9136

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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.1176	1.0	1150	0.3566	0.9181
0.0582	2.0	2300	0.4509	0.9136

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.2
• Datasets 2.12.0
• Tokenizers 0.13.3