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mpnet-adaptation_mitigation-classifier

This model is a fine-tuned version of sentence-transformers/all-mpnet-base-v2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2117
  • Precision Micro: 0.9175
  • Precision Weighted: 0.9181
  • Precision Samples: 0.9256
  • Recall Micro: 0.9281
  • Recall Weighted: 0.9281
  • Recall Samples: 0.9314
  • F1-score: 0.9263
  • Accuracy: 0.9082

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: 8e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 200
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Precision Micro Precision Weighted Precision Samples Recall Micro Recall Weighted Recall Samples F1-score Accuracy
0.3291 1.0 1051 0.2117 0.9175 0.9181 0.9256 0.9281 0.9281 0.9314 0.9263 0.9082

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.0+cu118
  • Datasets 2.11.0
  • Tokenizers 0.13.3
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