mpnet-adaptation_mitigation-classifier

• 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

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• 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

Framework versions

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