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--- |
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library_name: transformers |
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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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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: xmlNer-biobert |
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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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# xmlNer-biobert |
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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: 0.0843 |
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- Precision: 0.9481 |
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- Recall: 0.9752 |
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- F1: 0.9615 |
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- Accuracy: 0.9816 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.4975 | 1.0 | 612 | 0.1100 | 0.9268 | 0.9546 | 0.9405 | 0.9713 | |
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| 0.1263 | 2.0 | 1224 | 0.0902 | 0.9359 | 0.9720 | 0.9536 | 0.9776 | |
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| 0.0894 | 3.0 | 1836 | 0.0861 | 0.9437 | 0.9725 | 0.9579 | 0.9790 | |
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| 0.0687 | 4.0 | 2448 | 0.0841 | 0.9461 | 0.9748 | 0.9603 | 0.9809 | |
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| 0.0497 | 5.0 | 3060 | 0.0843 | 0.9481 | 0.9752 | 0.9615 | 0.9816 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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