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--- |
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library_name: transformers |
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license: mit |
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base_model: xlm-roberta-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: multilabel_transfer_learning_transformer |
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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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# multilabel_transfer_learning_transformer |
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0217 |
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- F1: 0.9924 |
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- Roc Auc: 0.9955 |
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- Accuracy: 0.9887 |
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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: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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- seed: 123 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 300 |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| 0.5454 | 1.0 | 136 | 0.4135 | 0.0125 | 0.5030 | 0.0 | |
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| 0.3917 | 2.0 | 272 | 0.3582 | 0.2939 | 0.5855 | 0.0338 | |
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| 0.3405 | 3.0 | 408 | 0.3048 | 0.4862 | 0.6649 | 0.0827 | |
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| 0.2918 | 4.0 | 544 | 0.2753 | 0.5913 | 0.7250 | 0.1278 | |
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| 0.2531 | 5.0 | 680 | 0.2285 | 0.7261 | 0.8065 | 0.2406 | |
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| 0.214 | 6.0 | 816 | 0.1971 | 0.7684 | 0.8328 | 0.3233 | |
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| 0.181 | 7.0 | 952 | 0.1663 | 0.8199 | 0.8624 | 0.4173 | |
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| 0.1529 | 8.0 | 1088 | 0.1431 | 0.8591 | 0.8905 | 0.4774 | |
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| 0.1307 | 9.0 | 1224 | 0.1224 | 0.8979 | 0.9260 | 0.6090 | |
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| 0.1108 | 10.0 | 1360 | 0.1034 | 0.9195 | 0.9329 | 0.6955 | |
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| 0.0961 | 11.0 | 1496 | 0.0920 | 0.9435 | 0.9553 | 0.7744 | |
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| 0.0821 | 12.0 | 1632 | 0.0793 | 0.9559 | 0.9627 | 0.8346 | |
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| 0.0719 | 13.0 | 1768 | 0.0682 | 0.9636 | 0.9732 | 0.8759 | |
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| 0.0612 | 14.0 | 1904 | 0.0618 | 0.9651 | 0.9760 | 0.8947 | |
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| 0.0526 | 15.0 | 2040 | 0.0519 | 0.9757 | 0.9796 | 0.9135 | |
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| 0.0456 | 16.0 | 2176 | 0.0468 | 0.9778 | 0.9835 | 0.9248 | |
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| 0.0394 | 17.0 | 2312 | 0.0396 | 0.9854 | 0.9885 | 0.9586 | |
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| 0.0343 | 18.0 | 2448 | 0.0372 | 0.9855 | 0.9911 | 0.9586 | |
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| 0.0299 | 19.0 | 2584 | 0.0329 | 0.9854 | 0.9885 | 0.9586 | |
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| 0.0266 | 20.0 | 2720 | 0.0289 | 0.9887 | 0.9932 | 0.9887 | |
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| 0.0233 | 21.0 | 2856 | 0.0264 | 0.9874 | 0.9919 | 0.9812 | |
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| 0.0212 | 22.0 | 2992 | 0.0258 | 0.9887 | 0.9932 | 0.9887 | |
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| 0.02 | 23.0 | 3128 | 0.0242 | 0.9887 | 0.9932 | 0.9887 | |
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| 0.0177 | 24.0 | 3264 | 0.0217 | 0.9924 | 0.9955 | 0.9887 | |
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| 0.0162 | 25.0 | 3400 | 0.0200 | 0.9887 | 0.9932 | 0.9887 | |
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| 0.0146 | 26.0 | 3536 | 0.0201 | 0.9906 | 0.9951 | 0.9887 | |
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| 0.0136 | 27.0 | 3672 | 0.0192 | 0.9906 | 0.9951 | 0.9887 | |
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| 0.0127 | 28.0 | 3808 | 0.0169 | 0.9924 | 0.9955 | 0.9887 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.19.1 |
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