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ag-news-twitter-76800-bert-base-uncased

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

  • F1: 0.9415
  • Acc: 0.9416
  • Loss: 0.5192

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: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 20

Training results

Training Loss Epoch Step F1 Acc Validation Loss
0.2328 1.0 4800 0.9289 0.9289 0.2082
0.2061 2.0 9600 0.9366 0.9367 0.2154
0.1488 3.0 14400 0.9401 0.9401 0.2181
0.114 4.0 19200 0.9280 0.9275 0.3199
0.0818 5.0 24000 0.9399 0.94 0.2953
0.051 6.0 28800 0.9402 0.9403 0.3828
0.0413 7.0 33600 0.9404 0.9403 0.4327
0.0342 8.0 38400 0.9395 0.9395 0.4291
0.0192 9.0 43200 0.9422 0.9422 0.4170
0.0204 10.0 48000 0.9374 0.9374 0.4761
0.0125 11.0 52800 0.9358 0.9359 0.5126
0.0124 12.0 57600 0.9415 0.9416 0.5192

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu121
  • Datasets 2.14.6
  • Tokenizers 0.14.1
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