| | --- |
| | language: |
| | - nl |
| | license: apache-2.0 |
| | base_model: bert-base-uncased |
| | tags: |
| | - abc |
| | - generated_from_trainer |
| | datasets: |
| | - stsb_multi_mt |
| | model-index: |
| | - name: bert-base-uncased-FinedTuned |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # bert-base-uncased-FinedTuned |
| |
|
| | This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/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 |
| |
|
