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--- |
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license: mit |
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base_model: nielsr/lilt-xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: test |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# test |
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This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6527 |
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- Precision: 0.7393 |
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- Recall: 0.7759 |
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- F1: 0.7571 |
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- Accuracy: 0.7614 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.3333 | 100 | 0.9776 | 0.4894 | 0.6113 | 0.5436 | 0.6166 | |
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| No log | 2.6667 | 200 | 0.8649 | 0.6249 | 0.6296 | 0.6273 | 0.7231 | |
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| No log | 4.0 | 300 | 0.8745 | 0.6449 | 0.7392 | 0.6888 | 0.7326 | |
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| No log | 5.3333 | 400 | 0.9419 | 0.6292 | 0.7168 | 0.6702 | 0.7367 | |
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| 0.6362 | 6.6667 | 500 | 0.9902 | 0.7090 | 0.7458 | 0.7269 | 0.7700 | |
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| 0.6362 | 8.0 | 600 | 1.0048 | 0.7050 | 0.7315 | 0.7180 | 0.7614 | |
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| 0.6362 | 9.3333 | 700 | 1.1327 | 0.6918 | 0.7305 | 0.7106 | 0.7568 | |
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| 0.6362 | 10.6667 | 800 | 1.3954 | 0.6952 | 0.7366 | 0.7153 | 0.7333 | |
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| 0.6362 | 12.0 | 900 | 1.2721 | 0.7002 | 0.7509 | 0.7247 | 0.7491 | |
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| 0.1105 | 13.3333 | 1000 | 1.3422 | 0.7166 | 0.7356 | 0.7260 | 0.7521 | |
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| 0.1105 | 14.6667 | 1100 | 1.3957 | 0.72 | 0.7427 | 0.7312 | 0.7605 | |
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| 0.1105 | 16.0 | 1200 | 1.4581 | 0.7250 | 0.7519 | 0.7382 | 0.7608 | |
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| 0.1105 | 17.3333 | 1300 | 1.4598 | 0.7459 | 0.7371 | 0.7415 | 0.7577 | |
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| 0.1105 | 18.6667 | 1400 | 1.5542 | 0.7182 | 0.7504 | 0.7339 | 0.7497 | |
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| 0.0271 | 20.0 | 1500 | 1.5411 | 0.7176 | 0.7779 | 0.7465 | 0.7549 | |
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| 0.0271 | 21.3333 | 1600 | 1.6468 | 0.7251 | 0.7539 | 0.7393 | 0.7452 | |
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| 0.0271 | 22.6667 | 1700 | 1.6821 | 0.7311 | 0.7550 | 0.7429 | 0.7487 | |
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| 0.0271 | 24.0 | 1800 | 1.6220 | 0.7364 | 0.7499 | 0.7431 | 0.7585 | |
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| 0.0271 | 25.3333 | 1900 | 1.6220 | 0.7403 | 0.7667 | 0.7533 | 0.7599 | |
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| 0.0055 | 26.6667 | 2000 | 1.6161 | 0.7370 | 0.7682 | 0.7523 | 0.7687 | |
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| 0.0055 | 28.0 | 2100 | 1.6683 | 0.7396 | 0.7713 | 0.7551 | 0.7599 | |
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| 0.0055 | 29.3333 | 2200 | 1.6527 | 0.7393 | 0.7759 | 0.7571 | 0.7614 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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