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--- |
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license: mit |
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base_model: FacebookAI/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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: Vic_model2 |
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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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# Vic_model2 |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/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: 0.2487 |
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- Accuracy: 0.9657 |
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- Precision: 0.9663 |
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- Recall: 0.9657 |
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- F1: 0.9654 |
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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: 5e-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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.8139 | 1.0 | 1313 | 0.6269 | 0.83 | 0.8370 | 0.8300 | 0.8242 | |
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| 0.4671 | 2.0 | 2626 | 0.5028 | 0.8786 | 0.8837 | 0.8786 | 0.8757 | |
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| 0.343 | 3.0 | 3939 | 0.4058 | 0.8957 | 0.9038 | 0.8957 | 0.8965 | |
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| 0.222 | 4.0 | 5252 | 0.4109 | 0.9286 | 0.9295 | 0.9286 | 0.9274 | |
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| 0.1237 | 5.0 | 6565 | 0.3822 | 0.9357 | 0.9387 | 0.9357 | 0.9354 | |
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| 0.0629 | 6.0 | 7878 | 0.3639 | 0.9429 | 0.9459 | 0.9429 | 0.9433 | |
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| 0.0186 | 7.0 | 9191 | 0.2977 | 0.9557 | 0.9567 | 0.9557 | 0.9555 | |
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| 0.0104 | 8.0 | 10504 | 0.2487 | 0.9657 | 0.9663 | 0.9657 | 0.9654 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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