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README.md
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---
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license: mit
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: finetuned_roberta-base-uncased
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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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# finetuned_roberta-base-uncased
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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: 1.4799
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- Accuracy: 0.6519
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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: 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: 20
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 1.372 | 1.0 | 102 | 1.3643 | 0.3375 |
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| 1.1591 | 2.0 | 204 | 1.1988 | 0.4830 |
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| 0.9623 | 3.0 | 306 | 1.0802 | 0.5694 |
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| 0.7766 | 4.0 | 408 | 0.9885 | 0.6237 |
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| 0.7336 | 5.0 | 510 | 1.0393 | 0.6120 |
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| 0.6284 | 6.0 | 612 | 1.1150 | 0.6392 |
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| 0.3616 | 7.0 | 714 | 1.2183 | 0.6402 |
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| 0.3526 | 8.0 | 816 | 1.2362 | 0.6305 |
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| 0.3151 | 9.0 | 918 | 1.3058 | 0.6372 |
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| 0.3035 | 10.0 | 1020 | 1.2966 | 0.6343 |
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| 0.2458 | 11.0 | 1122 | 1.3752 | 0.6508 |
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| 0.2469 | 12.0 | 1224 | 1.4557 | 0.6557 |
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| 0.2039 | 13.0 | 1326 | 1.5541 | 0.6372 |
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| 0.1691 | 14.0 | 1428 | 1.5308 | 0.6343 |
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| 0.1455 | 15.0 | 1530 | 1.6339 | 0.6421 |
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| 0.1716 | 16.0 | 1632 | 1.6843 | 0.6392 |
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| 0.1698 | 17.0 | 1734 | 1.6802 | 0.6479 |
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| 0.2009 | 18.0 | 1836 | 1.6544 | 0.6479 |
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| 0.1415 | 19.0 | 1938 | 1.6759 | 0.6518 |
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| 0.1616 | 20.0 | 2040 | 1.6833 | 0.6508 |
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### Framework versions
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- Transformers 4.26.1
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- Pytorch 1.13.1+cu116
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- Datasets 2.10.1
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- Tokenizers 0.13.2
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