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Redaction Classifier: NLP Edition

This model is a fine-tuned version of microsoft/deberta-v3-small on a custom dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0893
  • Pearson: 0.8273

Model description

Read more about the process and the code used to train this model on my blog here.

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: 0.0001
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 6
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Pearson
0.2054 1.0 729 0.1382 0.6771
0.1386 2.0 1458 0.1099 0.7721
0.0782 3.0 2187 0.0950 0.8083
0.054 4.0 2916 0.0945 0.8185
0.0319 5.0 3645 0.0880 0.8251
0.0254 6.0 4374 0.0893 0.8273

Framework versions

  • Transformers 4.19.2
  • Pytorch 1.11.0a0+17540c5
  • Datasets 2.2.2
  • Tokenizers 0.12.1
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