| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: CR_XLNet_5E |
| 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. --> |
|
|
| # CR_XLNet_5E |
|
|
| This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6034 |
| - Accuracy: 0.9067 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
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|
| The following hyperparameters were used during training: |
| - learning_rate: 3e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 0.5384 | 0.33 | 50 | 0.4165 | 0.8533 | |
| | 0.3633 | 0.66 | 100 | 0.3059 | 0.8867 | |
| | 0.2642 | 0.99 | 150 | 0.2582 | 0.9267 | |
| | 0.2626 | 1.32 | 200 | 0.3324 | 0.9 | |
| | 0.1859 | 1.66 | 250 | 0.4076 | 0.9067 | |
| | 0.2631 | 1.99 | 300 | 0.4334 | 0.8867 | |
| | 0.1449 | 2.32 | 350 | 0.4264 | 0.9 | |
| | 0.1815 | 2.65 | 400 | 0.4334 | 0.8933 | |
| | 0.1316 | 2.98 | 450 | 0.4436 | 0.9 | |
| | 0.0725 | 3.31 | 500 | 0.6165 | 0.9 | |
| | 0.0708 | 3.64 | 550 | 0.6737 | 0.8933 | |
| | 0.0821 | 3.97 | 600 | 0.5777 | 0.9067 | |
| | 0.0381 | 4.3 | 650 | 0.6052 | 0.9 | |
| | 0.0441 | 4.64 | 700 | 0.5853 | 0.9133 | |
| | 0.0237 | 4.97 | 750 | 0.6034 | 0.9067 | |
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| ### Framework versions |
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|
| - Transformers 4.24.0 |
| - Pytorch 1.13.0 |
| - Datasets 2.3.2 |
| - Tokenizers 0.13.1 |
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|
