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

  • Loss: 0.4490
  • Accuracy: 0.8466
  • F1: 0.8065
  • Precision: 0.8406
  • Recall: 0.7876

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: 8
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
No log 1.0 267 0.3860 0.8370 0.7999 0.8184 0.7876
0.3455 2.0 534 0.4490 0.8466 0.8065 0.8406 0.7876

Framework versions

  • Transformers 4.37.0
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.1
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Safetensors
Model size
335M params
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F32
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Finetuned from

Dataset used to train Sifal/bertGED

Evaluation results