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README.md
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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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metrics:
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- accuracy
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model-index:
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- name: xnli_en_adalora_alpha_32_drop_01_rank_16
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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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# xnli_en_adalora_alpha_32_drop_01_rank_16
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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: 0.4301
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- Accuracy: 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: 0.0003
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- train_batch_size: 32
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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: 10.0
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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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| 0.5533 | 1.0 | 12272 | 0.4984 | 0.8020 |
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| 0.5074 | 2.0 | 24544 | 0.4872 | 0.8088 |
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| 0.481 | 3.0 | 36816 | 0.4989 | 0.8036 |
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| 0.4747 | 4.0 | 49088 | 0.4450 | 0.8213 |
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| 0.4547 | 5.0 | 61360 | 0.4395 | 0.8349 |
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| 0.4413 | 6.0 | 73632 | 0.4363 | 0.8329 |
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| 0.4147 | 7.0 | 85904 | 0.4383 | 0.8269 |
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| 0.4252 | 8.0 | 98176 | 0.4266 | 0.8414 |
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| 0.4197 | 9.0 | 110448 | 0.4298 | 0.8430 |
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| 0.4065 | 10.0 | 122720 | 0.4301 | 0.8382 |
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### Framework versions
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- Transformers 4.41.1
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- Pytorch 2.3.0+cu121
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- Datasets 2.18.0
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- Tokenizers 0.19.1
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