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--- |
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license: mit |
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base_model: xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: XLM-R-BASE-Finetune-step2-finetune-and-eval-may31-volcanic-moon-1-D-05-31-T-03-38 |
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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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# XLM-R-BASE-Finetune-step2-finetune-and-eval-may31-volcanic-moon-1-D-05-31-T-03-38 |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/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: 0.3977 |
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- Precision 0: 0.8721 |
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- Precision 1: 0.8029 |
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- Recall 0: 0.8633 |
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- Recall 1: 0.8148 |
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- F1 0: 0.8677 |
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- F1 1: 0.8088 |
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- Precision Weighted: 0.8440 |
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- Recall Weighted: 0.8436 |
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- F1 Weighted: 0.8438 |
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- F1 Macro: 0.8382 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision 0 | Precision 1 | Recall 0 | Recall 1 | F1 0 | F1 1 | Precision Weighted | Recall Weighted | F1 Weighted | F1 Macro | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-----------:|:--------:|:--------:|:------:|:------:|:------------------:|:---------------:|:-----------:|:--------:| |
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| 0.5501 | 1.0 | 469 | 0.4524 | 0.7699 | 0.8995 | 0.9556 | 0.5823 | 0.8528 | 0.7069 | 0.8226 | 0.804 | 0.7936 | 0.7799 | |
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| 0.3869 | 2.0 | 938 | 0.4545 | 0.8995 | 0.7229 | 0.7710 | 0.8739 | 0.8303 | 0.7913 | 0.8278 | 0.8128 | 0.8145 | 0.8108 | |
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| 0.3825 | 3.0 | 1407 | 0.3678 | 0.8429 | 0.8191 | 0.8855 | 0.7586 | 0.8637 | 0.7877 | 0.8333 | 0.834 | 0.8329 | 0.8257 | |
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| 0.2683 | 4.0 | 1876 | 0.3977 | 0.8721 | 0.8029 | 0.8633 | 0.8148 | 0.8677 | 0.8088 | 0.8440 | 0.8436 | 0.8438 | 0.8382 | |
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| 0.2297 | 5.0 | 2345 | 0.5155 | 0.8711 | 0.7937 | 0.8552 | 0.8148 | 0.8631 | 0.8041 | 0.8396 | 0.8388 | 0.8391 | 0.8336 | |
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### Framework versions |
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- Transformers 4.41.2 |
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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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