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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: 20230928-10-xlm-roberta-base-new |
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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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# 20230928-10-xlm-roberta-base-new |
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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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- Accuracy: 0.4847 |
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- Loss: nan |
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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: 8 |
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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 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | Validation Loss | |
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|:-------------:|:-----:|:----:|:--------:|:---------------:| |
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| 4.5116 | 0.46 | 200 | 0.2970 | nan | |
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| 3.9887 | 0.91 | 400 | 0.3086 | 4.0522 | |
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| 3.9101 | 1.37 | 600 | 0.3138 | nan | |
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| 3.7634 | 1.82 | 800 | 0.3184 | 3.7682 | |
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| 3.4413 | 2.28 | 1000 | 0.3977 | 2.9317 | |
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| 3.4478 | 2.73 | 1200 | 0.4102 | nan | |
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| 3.4179 | 3.19 | 1400 | 0.3790 | 3.2163 | |
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| 3.2165 | 3.64 | 1600 | 0.4435 | 2.7062 | |
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| 3.1388 | 4.1 | 1800 | 0.4540 | 2.8629 | |
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| 3.0987 | 4.56 | 2000 | 0.4509 | nan | |
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| 3.0614 | 5.01 | 2200 | 0.4894 | nan | |
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| 3.1399 | 5.47 | 2400 | 0.5337 | nan | |
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| 3.0387 | 5.92 | 2600 | 0.4667 | 3.0331 | |
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| 2.9728 | 6.38 | 2800 | 0.4571 | nan | |
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| 2.9138 | 6.83 | 3000 | 0.4306 | 2.8179 | |
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| 2.9157 | 7.29 | 3200 | 0.4520 | nan | |
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| 2.8103 | 7.74 | 3400 | 0.5 | nan | |
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| 2.7693 | 8.2 | 3600 | 0.4841 | 2.9349 | |
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| 2.8204 | 8.66 | 3800 | 0.4727 | 2.7438 | |
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| 2.7711 | 9.11 | 4000 | 0.5123 | 2.6654 | |
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| 2.7196 | 9.57 | 4200 | 0.4847 | nan | |
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### Framework versions |
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- Transformers 4.33.3 |
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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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