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TenaliAI-FinTech-v1

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

  • Loss: 0.8406

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: 25

Training results

Training Loss Epoch Step Validation Loss
2.3809 1.0 3852 1.9607
1.2279 2.0 7704 1.1452
0.9366 3.0 11556 0.9717
0.8443 4.0 15408 0.8946
0.795 5.0 19260 0.8747
0.736 6.0 23112 0.8618
0.7126 7.0 26964 0.8406
0.6937 8.0 30816 0.8497
0.6922 9.0 34668 0.8473
0.6521 10.0 38520 0.8563
0.6838 11.0 42372 0.8638
0.6395 12.0 46224 0.8615
0.6551 13.0 50076 0.8654
0.6038 14.0 53928 0.8795
0.6177 15.0 57780 0.8782
0.6164 16.0 61632 0.8914
0.6071 17.0 65484 0.8859
0.6125 18.0 69336 0.8921
0.6115 19.0 73188 0.8944
0.608 20.0 77040 0.9040
0.5943 21.0 80892 0.9133
0.632 22.0 84744 0.9031
0.6245 23.0 88596 0.9139
0.6094 24.0 92448 0.8965
0.6247 25.0 96300 0.9290

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1
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