bert-base-uncased-yahoo_answers_topics

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

  • Loss: 0.8092
  • Accuracy: 0.7499

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 86625
  • training_steps: 866250

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.162 0.01 2000 1.7444 0.5681
1.3126 0.02 4000 1.0081 0.7054
0.9592 0.03 6000 0.9021 0.7234
0.8903 0.05 8000 0.8827 0.7276
0.8685 0.06 10000 0.8540 0.7341
0.8422 0.07 12000 0.8547 0.7365
0.8535 0.08 14000 0.8264 0.7372
0.8178 0.09 16000 0.8331 0.7389
0.8325 0.1 18000 0.8242 0.7411
0.8181 0.12 20000 0.8356 0.7437
0.8171 0.13 22000 0.8090 0.7451
0.8092 0.14 24000 0.8469 0.7392
0.8057 0.15 26000 0.8185 0.7478
0.8085 0.16 28000 0.8090 0.7467
0.8229 0.17 30000 0.8225 0.7417
0.8151 0.18 32000 0.8262 0.7419
0.81 0.2 34000 0.8149 0.7383
0.8073 0.21 36000 0.8225 0.7441
0.816 0.22 38000 0.8037 0.744
0.8217 0.23 40000 0.8409 0.743
0.82 0.24 42000 0.8286 0.7385
0.8101 0.25 44000 0.8282 0.7413
0.8254 0.27 46000 0.8170 0.7414

Framework versions

  • Transformers 4.10.2
  • Pytorch 1.7.1
  • Datasets 1.6.1
  • Tokenizers 0.10.3
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