| --- |
| library_name: transformers |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: reverse_add_replicate_eval17_corrupted |
| 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. --> |
|
|
| # reverse_add_replicate_eval17_corrupted |
|
|
| This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5592 |
| - Accuracy: 0.0 |
|
|
| ## 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: 0.001 |
| - train_batch_size: 64 |
| - eval_batch_size: 64 |
| - seed: 7658372 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 128 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 1 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| |
| | No log | 0 | 0 | 2.7152 | 0.0 | |
| | 4.1279 | 0.0233 | 100 | 2.4441 | 0.0 | |
| | 3.8869 | 0.0465 | 200 | 2.2535 | 0.0 | |
| | 3.7932 | 0.0698 | 300 | 2.2601 | 0.0 | |
| | 4.0653 | 0.0931 | 400 | 2.3063 | 0.0 | |
| | 3.5939 | 0.1164 | 500 | 2.1550 | 0.0 | |
| | 3.5464 | 0.1396 | 600 | 2.1346 | 0.0 | |
| | 2.7715 | 0.1629 | 700 | 1.9327 | 0.0 | |
| | 2.949 | 0.1862 | 800 | 1.7166 | 0.0 | |
| | 2.4314 | 0.2094 | 900 | 1.5630 | 0.0 | |
| | 2.384 | 0.2327 | 1000 | 1.3745 | 0.0 | |
| | 2.4366 | 0.2560 | 1100 | 1.4244 | 0.0 | |
| | 2.1071 | 0.2793 | 1200 | 1.3338 | 0.0 | |
| | 2.1589 | 0.3025 | 1300 | 1.2461 | 0.0 | |
| | 2.3178 | 0.3258 | 1400 | 1.3081 | 0.0 | |
| | 1.9503 | 0.3491 | 1500 | 1.3001 | 0.001 | |
| | 1.9743 | 0.3724 | 1600 | 1.2392 | 0.0 | |
| | 1.8305 | 0.3956 | 1700 | 1.3122 | 0.0 | |
| | 2.1996 | 0.4189 | 1800 | 1.2592 | 0.0 | |
| | 2.0105 | 0.4422 | 1900 | 1.2169 | 0.001 | |
| | 2.138 | 0.4654 | 2000 | 1.3759 | 0.0 | |
| | 2.1093 | 0.4887 | 2100 | 1.3241 | 0.0 | |
| | 1.9048 | 0.5120 | 2200 | 1.2938 | 0.0 | |
| | 2.0772 | 0.5353 | 2300 | 1.1998 | 0.0 | |
| | 1.8008 | 0.5585 | 2400 | 1.2685 | 0.0 | |
| | 1.9558 | 0.5818 | 2500 | 1.3011 | 0.0 | |
| | 1.9744 | 0.6051 | 2600 | 1.3717 | 0.0 | |
| | 1.9765 | 0.6283 | 2700 | 1.2421 | 0.0 | |
| | 2.0307 | 0.6516 | 2800 | 1.2278 | 0.0 | |
| | 1.9778 | 0.6749 | 2900 | 1.3581 | 0.0 | |
| | 1.7576 | 0.6982 | 3000 | 1.1796 | 0.0 | |
| | 1.9729 | 0.7214 | 3100 | 1.1137 | 0.003 | |
| | 1.6585 | 0.7447 | 3200 | 1.2091 | 0.0 | |
| | 1.2024 | 0.7680 | 3300 | 1.1949 | 0.0 | |
| | 0.7904 | 0.7912 | 3400 | 0.9786 | 0.008 | |
| | 0.6275 | 0.8145 | 3500 | 0.8475 | 0.001 | |
| | 0.3953 | 0.8378 | 3600 | 0.7642 | 0.0 | |
| | 0.1835 | 0.8611 | 3700 | 0.6556 | 0.0 | |
| | 0.111 | 0.8843 | 3800 | 0.6091 | 0.0 | |
| | 0.1189 | 0.9076 | 3900 | 0.6340 | 0.0 | |
| | 0.0729 | 0.9309 | 4000 | 0.6288 | 0.0 | |
| | 0.0609 | 0.9542 | 4100 | 0.5450 | 0.0 | |
| | 0.0449 | 0.9774 | 4200 | 0.5592 | 0.0 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.46.0 |
| - Pytorch 2.5.1 |
| - Datasets 3.1.0 |
| - Tokenizers 0.20.1 |
|
|
