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bert-base-uncased-FinedTuned

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bert-base-uncased-FinedTuned

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

  • Loss: 2.7758
  • Pearson: 0.2352
  • Mse: 2.7758
  • Custom Accuracy: 0.2611
  • Dataset Accuracy: 0.1762

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: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • training_steps: 12000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Pearson Mse Custom Accuracy Dataset Accuracy
0.028 5.5556 1000 2.7386 0.2467 2.7386 0.2502 0.1762
0.0269 11.1111 2000 2.8265 0.2229 2.8265 0.2589 0.1762
0.0088 16.6667 3000 2.8485 0.2219 2.8485 0.2654 0.1762
0.0141 22.2222 4000 2.8855 0.2086 2.8855 0.2661 0.1762
0.0099 27.7778 5000 2.8081 0.2328 2.8081 0.2632 0.1762
0.0248 33.3333 6000 2.7765 0.2309 2.7765 0.2625 0.1762
0.0353 38.8889 7000 2.8126 0.2296 2.8126 0.2748 0.1762
0.0892 44.4444 8000 2.8362 0.2327 2.8362 0.2567 0.1762
0.0488 50.0 9000 2.7667 0.2363 2.7667 0.2596 0.1762
0.0538 55.5556 10000 2.7885 0.2363 2.7885 0.2632 0.1762
0.0829 61.1111 11000 2.7837 0.2348 2.7837 0.2647 0.1762
0.1473 66.6667 12000 2.7758 0.2352 2.7758 0.2611 0.1762

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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Dataset used to train theCuiCoders/bert-base-uncased-FinedTuned

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