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rule_learning_margin_1mm

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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - enoriega/odinsynth_dataset
model-index:
  - name: rule_learning_margin_1mm
    results: []

rule_learning_margin_1mm

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

  • Loss: 0.3806
  • Margin Accuracy: 0.8239

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: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2000
  • total_train_batch_size: 8000
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Margin Accuracy
0.6482 0.16 20 0.6494 0.7263
0.5151 0.32 40 0.5088 0.7792
0.4822 0.48 60 0.4429 0.8045
0.4472 0.64 80 0.4265 0.8107
0.4352 0.8 100 0.4155 0.8132
0.4335 0.96 120 0.4128 0.8116
0.4113 1.12 140 0.4119 0.8142
0.4186 1.28 160 0.4075 0.8120
0.42 1.44 180 0.4072 0.8123
0.4175 1.6 200 0.4080 0.8130
0.4097 1.76 220 0.4031 0.8128
0.397 1.92 240 0.4004 0.8130
0.4115 2.08 260 0.3979 0.8136
0.4108 2.24 280 0.3940 0.8167
0.4125 2.4 300 0.3879 0.8218
0.4117 2.56 320 0.3848 0.8217
0.3967 2.72 340 0.3818 0.8231
0.3947 2.88 360 0.3813 0.8240

Framework versions

  • Transformers 4.19.2
  • Pytorch 1.11.0
  • Datasets 2.2.1
  • Tokenizers 0.12.1