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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