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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: 0.7477
  • Accuracy: 0.9176

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: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.2248 1.0 1150 0.2021 0.9172
0.182 2.0 2300 0.2216 0.9230
0.1301 3.0 3450 0.2681 0.9181
0.0985 4.0 4600 0.3468 0.9226
0.0651 5.0 5750 0.5141 0.9070
0.0332 6.0 6900 0.5732 0.9187
0.0266 7.0 8050 0.5991 0.9161
0.0129 8.0 9200 0.6872 0.9157
0.0095 9.0 10350 0.7212 0.9187
0.0023 10.0 11500 0.7477 0.9176

Framework versions

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