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--- |
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license: mit |
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base_model: xlm-roberta-large |
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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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model-index: |
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- name: fine-tuning-xlmr-large |
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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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# fine-tuning-xlmr-large |
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7558 |
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- Accuracy: 0.7692 |
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- Precision: 0.7692 |
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- Recall: 0.7692 |
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- F1 Score: 0.7693 |
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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: 1e-06 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 101 |
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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 | Precision | Recall | F1 Score | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:--------:| |
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| 1.3385 | 1.0 | 10330 | 1.8072 | 0.5708 | 0.5708 | 0.5708 | 0.5622 | |
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| 1.7231 | 2.0 | 20660 | 1.8354 | 0.6445 | 0.6445 | 0.6445 | 0.6454 | |
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| 1.4049 | 3.0 | 30990 | 1.8380 | 0.6969 | 0.6969 | 0.6969 | 0.6990 | |
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| 1.4543 | 4.0 | 41320 | 1.5726 | 0.7415 | 0.7415 | 0.7415 | 0.7417 | |
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| 1.4139 | 5.0 | 51650 | 1.6838 | 0.7424 | 0.7424 | 0.7424 | 0.7439 | |
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| 1.2368 | 6.0 | 61980 | 1.6794 | 0.7424 | 0.7424 | 0.7424 | 0.7448 | |
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| 1.0418 | 7.0 | 72310 | 1.6720 | 0.7542 | 0.7542 | 0.7542 | 0.7556 | |
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| 1.246 | 8.0 | 82640 | 1.6746 | 0.7638 | 0.7638 | 0.7638 | 0.7642 | |
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| 0.9896 | 9.0 | 92970 | 1.7497 | 0.7674 | 0.7674 | 0.7674 | 0.7666 | |
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| 0.9855 | 10.0 | 103300 | 1.7558 | 0.7692 | 0.7692 | 0.7692 | 0.7693 | |
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### Framework versions |
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- Transformers 4.36.0.dev0 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.0 |
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