whimsical-crane-88 / README.md
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stackoverflow_tag_classification/modernBERT_vs_Deberta/ModernBERT-base/whimsical-crane-88
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metadata
library_name: peft
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
  - generated_from_trainer
model-index:
  - name: whimsical-crane-88
    results: []

whimsical-crane-88

This model is a fine-tuned version of answerdotai/ModernBERT-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3017
  • Hamming Loss: 0.1008
  • Zero One Loss: 0.8638
  • Jaccard Score: 0.8619
  • Hamming Loss Optimised: 0.1005
  • Hamming Loss Threshold: 0.4669
  • Zero One Loss Optimised: 0.7662
  • Zero One Loss Threshold: 0.2371
  • Jaccard Score Optimised: 0.7019
  • Jaccard Score Threshold: 0.1424

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: 5.6029632367762424e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 2024
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.8378996868939299,0.8949526992950398) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss Hamming Loss Zero One Loss Jaccard Score Hamming Loss Optimised Hamming Loss Threshold Zero One Loss Optimised Zero One Loss Threshold Jaccard Score Optimised Jaccard Score Threshold
No log 1.0 100 0.3418 0.1123 0.99 0.99 0.1121 0.5223 0.8287 0.2215 0.7749 0.1699
No log 2.0 200 0.3238 0.1098 0.965 0.9644 0.1069 0.4149 0.7863 0.2162 0.7400 0.1647
No log 3.0 300 0.3082 0.1021 0.8775 0.8762 0.1014 0.4735 0.7688 0.2369 0.7141 0.1721
No log 4.0 400 0.3017 0.1008 0.8638 0.8619 0.1005 0.4669 0.7662 0.2371 0.7019 0.1424

Framework versions

  • PEFT 0.13.2
  • Transformers 4.48.0.dev0
  • Pytorch 2.5.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.21.0