| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| model-index: |
| - name: xlnet-base-cased_fold_2_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_2_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: 0.4858 |
| - F1: 0.7648 |
| |
| ## 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 | 290 | 0.4361 | 0.7404 | |
| | 0.4403 | 2.0 | 580 | 0.5363 | 0.7515 | |
| | 0.4403 | 3.0 | 870 | 0.4858 | 0.7648 | |
| | 0.2505 | 4.0 | 1160 | 0.7127 | 0.7612 | |
| | 0.2505 | 5.0 | 1450 | 0.8930 | 0.7554 | |
| | 0.1425 | 6.0 | 1740 | 0.9897 | 0.7580 | |
| | 0.0869 | 7.0 | 2030 | 1.2683 | 0.7615 | |
| | 0.0869 | 8.0 | 2320 | 1.4988 | 0.7343 | |
| | 0.0411 | 9.0 | 2610 | 1.5082 | 0.7492 | |
| | 0.0411 | 10.0 | 2900 | 1.4974 | 0.7450 | |
| | 0.0306 | 11.0 | 3190 | 1.5723 | 0.7435 | |
| | 0.0306 | 12.0 | 3480 | 1.8446 | 0.7432 | |
| | 0.0291 | 13.0 | 3770 | 1.7113 | 0.7639 | |
| | 0.0207 | 14.0 | 4060 | 1.8073 | 0.7394 | |
| | 0.0207 | 15.0 | 4350 | 1.7524 | 0.7585 | |
| | 0.0171 | 16.0 | 4640 | 1.8751 | 0.7374 | |
| | 0.0171 | 17.0 | 4930 | 1.7849 | 0.7561 | |
| | 0.0084 | 18.0 | 5220 | 1.8618 | 0.7441 | |
| | 0.0064 | 19.0 | 5510 | 1.9613 | 0.7437 | |
| | 0.0064 | 20.0 | 5800 | 1.8898 | 0.7430 | |
| | 0.006 | 21.0 | 6090 | 1.9889 | 0.7409 | |
| | 0.006 | 22.0 | 6380 | 1.9949 | 0.7488 | |
| | 0.0049 | 23.0 | 6670 | 1.9453 | 0.7488 | |
| | 0.0049 | 24.0 | 6960 | 1.9754 | 0.7472 | |
| | 0.002 | 25.0 | 7250 | 1.9946 | 0.7504 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.21.0 |
| - Pytorch 1.12.0+cu113 |
| - Datasets 2.4.0 |
| - Tokenizers 0.12.1 |
| |
