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fine-tuned-NLI-mnli_original-with-roberta-large

This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3381
  • Accuracy: 0.9053
  • F1: 0.9056

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: 1e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.3302 0.4997 1533 0.2972 0.8940 0.8934
0.3139 0.9993 3066 0.2751 0.9027 0.9030
0.242 1.4990 4599 0.2866 0.9030 0.9030
0.2627 1.9987 6132 0.2921 0.9065 0.9066
0.1818 2.4984 7665 0.3049 0.9035 0.9038
0.1932 2.9980 9198 0.3153 0.9039 0.9038
0.1457 3.4977 10731 0.3381 0.9053 0.9056

Framework versions

  • Transformers 4.42.3
  • Pytorch 1.13.1+cu117
  • Datasets 2.2.0
  • Tokenizers 0.19.1
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