| --- |
| license: mit |
| base_model: FacebookAI/xlm-roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: XMLRoberta_Dataset8kMeta |
| 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. --> |
|
|
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/phunganhsang123/huggingface/runs/idvfzc2q) |
| # XMLRoberta_Dataset8kMeta |
| |
| This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3777 |
| - Accuracy: 0.9464 |
| - F1: 0.9465 |
| |
| ## 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: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 64 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 20 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:| |
| | No log | 1.6260 | 200 | 0.2882 | 0.9243 | 0.9108 | |
| | 0.4946 | 3.2520 | 400 | 0.2445 | 0.9166 | 0.9073 | |
| | 0.1849 | 4.8780 | 600 | 0.2282 | 0.9447 | 0.9453 | |
| | 0.1256 | 6.5041 | 800 | 0.2781 | 0.9472 | 0.9459 | |
| | 0.0808 | 8.1301 | 1000 | 0.2434 | 0.9404 | 0.9422 | |
| | 0.0808 | 9.7561 | 1200 | 0.3441 | 0.9438 | 0.9436 | |
| | 0.0592 | 11.3821 | 1400 | 0.3017 | 0.9506 | 0.9499 | |
| | 0.0418 | 13.0081 | 1600 | 0.3410 | 0.9455 | 0.9446 | |
| | 0.0345 | 14.6341 | 1800 | 0.3515 | 0.9430 | 0.9434 | |
| | 0.0233 | 16.2602 | 2000 | 0.3690 | 0.9455 | 0.9458 | |
| | 0.0233 | 17.8862 | 2200 | 0.3746 | 0.9481 | 0.9480 | |
| | 0.0176 | 19.5122 | 2400 | 0.3777 | 0.9464 | 0.9465 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.42.4 |
| - Pytorch 2.1.2 |
| - Datasets 2.19.2 |
| - Tokenizers 0.19.1 |
|
|
