| --- |
| library_name: transformers |
| license: mit |
| base_model: FacebookAI/roberta-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: roberta-Reflections-goodareas-sweeps-current |
| 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. --> |
|
|
| # roberta-Reflections-goodareas-sweeps-current |
|
|
| This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1937 |
| - Accuracy: 0.8562 |
| - Precision: 0.3984 |
| - Recall: 0.5632 |
| - F1: 0.4667 |
|
|
| ## 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: 3.693911058164899e-06 |
| - train_batch_size: 32 |
| - eval_batch_size: 16 |
| - 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: 20 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.3925 | 1.0 | 52 | 0.1759 | 0.8883 | 0.0 | 0.0 | 0.0 | |
| | 0.3241 | 2.0 | 104 | 0.1606 | 0.8883 | 0.0 | 0.0 | 0.0 | |
| | 0.2914 | 3.0 | 156 | 0.1744 | 0.8883 | 0.0 | 0.0 | 0.0 | |
| | 0.2821 | 4.0 | 208 | 0.2609 | 0.8909 | 0.75 | 0.0345 | 0.0659 | |
| | 0.2739 | 5.0 | 260 | 0.1763 | 0.8935 | 0.75 | 0.0690 | 0.1263 | |
| | 0.2533 | 6.0 | 312 | 0.1390 | 0.8922 | 0.6154 | 0.0920 | 0.16 | |
| | 0.2482 | 7.0 | 364 | 0.2199 | 0.8755 | 0.4490 | 0.5057 | 0.4757 | |
| | 0.2362 | 8.0 | 416 | 0.2124 | 0.8652 | 0.4286 | 0.6207 | 0.5070 | |
| | 0.2375 | 9.0 | 468 | 0.1351 | 0.8973 | 0.5614 | 0.3678 | 0.4444 | |
| | 0.228 | 10.0 | 520 | 0.1650 | 0.8870 | 0.4945 | 0.5172 | 0.5056 | |
| | 0.2212 | 11.0 | 572 | 0.1771 | 0.8845 | 0.4851 | 0.5632 | 0.5213 | |
| | 0.2217 | 12.0 | 624 | 0.1756 | 0.8832 | 0.4792 | 0.5287 | 0.5027 | |
| | 0.2109 | 13.0 | 676 | 0.1942 | 0.8614 | 0.4118 | 0.5632 | 0.4757 | |
| | 0.2018 | 14.0 | 728 | 0.1795 | 0.8678 | 0.4298 | 0.5632 | 0.4876 | |
| | 0.2013 | 15.0 | 780 | 0.1817 | 0.8652 | 0.4211 | 0.5517 | 0.4776 | |
| | 0.1943 | 16.0 | 832 | 0.2071 | 0.8575 | 0.4077 | 0.6092 | 0.4885 | |
| | 0.2023 | 17.0 | 884 | 0.2143 | 0.8498 | 0.3897 | 0.6092 | 0.4753 | |
| | 0.1924 | 18.0 | 936 | 0.1966 | 0.8562 | 0.4031 | 0.5977 | 0.4815 | |
| | 0.183 | 19.0 | 988 | 0.1914 | 0.8614 | 0.4118 | 0.5632 | 0.4757 | |
| | 0.191 | 20.0 | 1040 | 0.1937 | 0.8562 | 0.3984 | 0.5632 | 0.4667 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.48.3 |
| - Pytorch 2.5.1+cu124 |
| - Datasets 2.21.0 |
| - Tokenizers 0.21.0 |
| |
