| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| model-index: |
| - name: xlnet-base-cased_fold_3_binary |
| 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. --> |
|
|
| # xlnet-base-cased_fold_3_binary |
| |
| This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.3616 |
| - F1: 0.7758 |
| |
| ## 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 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 25 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | No log | 1.0 | 289 | 0.4668 | 0.7666 | |
| | 0.4142 | 2.0 | 578 | 0.4259 | 0.7631 | |
| | 0.4142 | 3.0 | 867 | 0.6744 | 0.7492 | |
| | 0.235 | 4.0 | 1156 | 0.8879 | 0.7678 | |
| | 0.235 | 5.0 | 1445 | 1.0036 | 0.7639 | |
| | 0.1297 | 6.0 | 1734 | 1.1427 | 0.7616 | |
| | 0.0894 | 7.0 | 2023 | 1.2126 | 0.7626 | |
| | 0.0894 | 8.0 | 2312 | 1.5098 | 0.7433 | |
| | 0.0473 | 9.0 | 2601 | 1.3616 | 0.7758 | |
| | 0.0473 | 10.0 | 2890 | 1.5966 | 0.7579 | |
| | 0.0325 | 11.0 | 3179 | 1.6669 | 0.7508 | |
| | 0.0325 | 12.0 | 3468 | 1.7401 | 0.7437 | |
| | 0.0227 | 13.0 | 3757 | 1.7797 | 0.7515 | |
| | 0.0224 | 14.0 | 4046 | 1.7349 | 0.7418 | |
| | 0.0224 | 15.0 | 4335 | 1.7527 | 0.7595 | |
| | 0.0152 | 16.0 | 4624 | 1.7492 | 0.7634 | |
| | 0.0152 | 17.0 | 4913 | 1.8178 | 0.7628 | |
| | 0.0117 | 18.0 | 5202 | 1.7736 | 0.7688 | |
| | 0.0117 | 19.0 | 5491 | 1.8449 | 0.7704 | |
| | 0.0055 | 20.0 | 5780 | 1.8687 | 0.7652 | |
| | 0.0065 | 21.0 | 6069 | 1.8083 | 0.7669 | |
| | 0.0065 | 22.0 | 6358 | 1.8568 | 0.7559 | |
| | 0.0054 | 23.0 | 6647 | 1.8760 | 0.7678 | |
| | 0.0054 | 24.0 | 6936 | 1.8948 | 0.7697 | |
| | 0.0048 | 25.0 | 7225 | 1.9109 | 0.7680 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.21.0 |
| - Pytorch 1.12.0+cu113 |
| - Datasets 2.4.0 |
| - Tokenizers 0.12.1 |
| |
