Instructions to use macbeeth/calculator_model_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use macbeeth/calculator_model_test with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("macbeeth/calculator_model_test") model = AutoModelForSeq2SeqLM.from_pretrained("macbeeth/calculator_model_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: calculator_model_test | |
| 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. --> | |
| # calculator_model_test | |
| This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.4280 | |
| ## 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: 512 | |
| - eval_batch_size: 512 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 40 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 2.9289 | 1.0 | 6 | 2.2631 | | |
| | 2.0248 | 2.0 | 12 | 1.8559 | | |
| | 1.6028 | 3.0 | 18 | 1.7034 | | |
| | 1.2773 | 4.0 | 24 | 1.6626 | | |
| | 1.0862 | 5.0 | 30 | 1.6454 | | |
| | 0.9687 | 6.0 | 36 | 1.8670 | | |
| | 0.8858 | 7.0 | 42 | 1.8190 | | |
| | 0.7782 | 8.0 | 48 | 2.0888 | | |
| | 0.7379 | 9.0 | 54 | 2.1530 | | |
| | 0.7093 | 10.0 | 60 | 2.1941 | | |
| | 0.6654 | 11.0 | 66 | 2.0807 | | |
| | 0.6296 | 12.0 | 72 | 2.1993 | | |
| | 0.5963 | 13.0 | 78 | 2.2719 | | |
| | 0.5661 | 14.0 | 84 | 2.2821 | | |
| | 0.5392 | 15.0 | 90 | 2.2940 | | |
| | 0.5209 | 16.0 | 96 | 2.3435 | | |
| | 0.4862 | 17.0 | 102 | 2.3659 | | |
| | 0.4886 | 18.0 | 108 | 2.4129 | | |
| | 0.4668 | 19.0 | 114 | 2.5027 | | |
| | 0.4577 | 20.0 | 120 | 2.5144 | | |
| | 0.4426 | 21.0 | 126 | 2.4393 | | |
| | 0.4198 | 22.0 | 132 | 2.4533 | | |
| | 0.4200 | 23.0 | 138 | 2.4996 | | |
| | 0.3980 | 24.0 | 144 | 2.4961 | | |
| | 0.3715 | 25.0 | 150 | 2.4773 | | |
| | 0.3642 | 26.0 | 156 | 2.4326 | | |
| | 0.3530 | 27.0 | 162 | 2.4626 | | |
| | 0.3300 | 28.0 | 168 | 2.4540 | | |
| | 0.3190 | 29.0 | 174 | 2.4593 | | |
| | 0.3125 | 30.0 | 180 | 2.4535 | | |
| | 0.3008 | 31.0 | 186 | 2.4917 | | |
| | 0.2989 | 32.0 | 192 | 2.4753 | | |
| | 0.2802 | 33.0 | 198 | 2.4765 | | |
| | 0.2677 | 34.0 | 204 | 2.4505 | | |
| | 0.2569 | 35.0 | 210 | 2.4462 | | |
| | 0.2535 | 36.0 | 216 | 2.4243 | | |
| | 0.2586 | 37.0 | 222 | 2.4185 | | |
| | 0.2421 | 38.0 | 228 | 2.4217 | | |
| | 0.2463 | 39.0 | 234 | 2.4261 | | |
| | 0.2430 | 40.0 | 240 | 2.4280 | | |
| ### Framework versions | |
| - Transformers 5.2.0 | |
| - Pytorch 2.10.0 | |
| - Datasets 4.6.1 | |
| - Tokenizers 0.22.2 | |