| --- |
| license: mit |
| base_model: xlnet-base-cased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: XLNet-Reddit-Toxic-Comment-Classification |
| 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-Reddit-Toxic-Comment-Classification |
|
|
| 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: 0.2248 |
| - Rmse: 0.2928 |
| - Accuracy: 0.9143 |
| - Precision: 0.9299 |
| - Recall: 0.9143 |
| - F1: 0.9220 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
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|
| 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: 8 |
| - 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 | Rmse | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|:------:| |
| | 0.3656 | 1.0 | 1073 | 0.2248 | 0.2928 | 0.9143 | 0.9299 | 0.9143 | 0.9220 | |
| | 0.2432 | 2.0 | 2146 | 0.3105 | 0.2912 | 0.9152 | 0.9158 | 0.9328 | 0.9242 | |
| | 0.1649 | 3.0 | 3219 | 0.3818 | 0.2696 | 0.9273 | 0.9176 | 0.9546 | 0.9357 | |
| | 0.1075 | 4.0 | 4292 | 0.4398 | 0.2798 | 0.9217 | 0.9049 | 0.9597 | 0.9315 | |
| | 0.0788 | 5.0 | 5365 | 0.4655 | 0.2847 | 0.9189 | 0.9110 | 0.9462 | 0.9283 | |
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| ### Framework versions |
|
|
| - Transformers 4.35.0.dev0 |
| - Pytorch 2.0.0 |
| - Datasets 2.1.0 |
| - Tokenizers 0.14.1 |
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|
