upset-auk-708 / README.md
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stackoverflow_tag_classification/modernBERT_vs_Deberta/ModernBERT-base/upset-auk-708
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metadata
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
  - generated_from_trainer
model-index:
  - name: upset-auk-708
    results: []

upset-auk-708

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: 0.3263
  • Hamming Loss: 0.1113
  • Zero One Loss: 0.9875
  • Jaccard Score: 0.9869
  • Hamming Loss Optimised: 0.1077
  • Hamming Loss Threshold: 0.3523
  • Zero One Loss Optimised: 0.785
  • Zero One Loss Threshold: 0.2199
  • Jaccard Score Optimised: 0.7435
  • Jaccard Score Threshold: 0.2046

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: 1.090012056785563e-06
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 2024
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9422410857324217,0.913862773872536) 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 0.4186 0.1174 0.9862 0.9842 0.1123 0.7028 0.9275 0.3569 0.8325 0.3103
No log 2.0 200 0.3414 0.1125 0.9988 0.9988 0.1123 0.5944 0.8362 0.2346 0.7740 0.2016
No log 3.0 300 0.3295 0.1116 0.9912 0.9912 0.1091 0.3329 0.7875 0.2167 0.7499 0.2120
No log 4.0 400 0.3263 0.1113 0.9875 0.9869 0.1077 0.3523 0.785 0.2199 0.7435 0.2046

Framework versions

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