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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-base |
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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: xlm-roberta-base-pcm-noaug |
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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-roberta-base-pcm-noaug |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5261 |
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- F1: 0.4912 |
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- Roc Auc: 0.6688 |
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- Accuracy: 0.3065 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 20 |
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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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| 0.4984 | 1.0 | 117 | 0.4862 | 0.1155 | 0.5131 | 0.2081 | |
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| 0.456 | 2.0 | 234 | 0.4323 | 0.2110 | 0.5565 | 0.2145 | |
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| 0.4207 | 3.0 | 351 | 0.4142 | 0.2856 | 0.5909 | 0.2903 | |
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| 0.3932 | 4.0 | 468 | 0.4189 | 0.3215 | 0.6145 | 0.2984 | |
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| 0.3576 | 5.0 | 585 | 0.4417 | 0.3400 | 0.6254 | 0.3048 | |
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| 0.324 | 6.0 | 702 | 0.4158 | 0.4024 | 0.6332 | 0.3129 | |
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| 0.3011 | 7.0 | 819 | 0.4176 | 0.4393 | 0.6516 | 0.3016 | |
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| 0.2675 | 8.0 | 936 | 0.4433 | 0.4546 | 0.6663 | 0.3290 | |
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| 0.2436 | 9.0 | 1053 | 0.4513 | 0.4435 | 0.6547 | 0.3258 | |
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| 0.2169 | 10.0 | 1170 | 0.4674 | 0.4624 | 0.6641 | 0.3177 | |
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| 0.2241 | 11.0 | 1287 | 0.4843 | 0.4706 | 0.6649 | 0.2984 | |
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| 0.1853 | 12.0 | 1404 | 0.4866 | 0.4646 | 0.6601 | 0.3323 | |
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| 0.1751 | 13.0 | 1521 | 0.5068 | 0.4555 | 0.6557 | 0.3081 | |
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| 0.1596 | 14.0 | 1638 | 0.4991 | 0.4640 | 0.6571 | 0.3145 | |
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| 0.1458 | 15.0 | 1755 | 0.5174 | 0.4784 | 0.6667 | 0.3210 | |
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| 0.1446 | 16.0 | 1872 | 0.5261 | 0.4912 | 0.6688 | 0.3065 | |
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| 0.1502 | 17.0 | 1989 | 0.5211 | 0.4876 | 0.6669 | 0.3242 | |
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| 0.1309 | 18.0 | 2106 | 0.5219 | 0.4823 | 0.6646 | 0.3242 | |
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| 0.1419 | 19.0 | 2223 | 0.5247 | 0.4767 | 0.6626 | 0.3258 | |
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| 0.138 | 20.0 | 2340 | 0.5241 | 0.4793 | 0.6631 | 0.3242 | |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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