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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: xlm-roberta-base-lora-text-classification |
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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-lora-text-classification |
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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: 0.5536 |
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- Accuracy: {'accuracy': 0.8980555555555556} |
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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: 0.001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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 |
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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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| 0.4165 | 1.0 | 3600 | 0.4468 | {'accuracy': 0.895} | |
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| 0.5475 | 2.0 | 7200 | 0.4904 | {'accuracy': 0.8897222222222222} | |
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| 0.5878 | 3.0 | 10800 | 0.4564 | {'accuracy': 0.8986111111111111} | |
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| 0.6508 | 4.0 | 14400 | 0.4560 | {'accuracy': 0.8916666666666667} | |
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| 0.5914 | 5.0 | 18000 | 0.5437 | {'accuracy': 0.8883333333333333} | |
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| 0.5927 | 6.0 | 21600 | 0.5986 | {'accuracy': 0.8841666666666667} | |
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| 0.6044 | 7.0 | 25200 | 0.6523 | {'accuracy': 0.8791666666666667} | |
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| 0.6249 | 8.0 | 28800 | 0.6199 | {'accuracy': 0.8833333333333333} | |
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| 0.5427 | 9.0 | 32400 | 0.5363 | {'accuracy': 0.8969444444444444} | |
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| 0.5724 | 10.0 | 36000 | 0.5536 | {'accuracy': 0.8980555555555556} | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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