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--- |
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library_name: transformers |
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base_model: FacebookAI/xlm-roberta-large-finetuned-conll03-english |
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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: xlm-roberta-large-finetuned-conll03-english-finetuned-ner-biomedical-spanish |
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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-large-finetuned-conll03-english-finetuned-ner-biomedical-spanish |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1526 |
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- Precision: 0.8568 |
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- Recall: 0.8258 |
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- F1: 0.8410 |
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- Accuracy: 0.9542 |
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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-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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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 200 |
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- num_epochs: 10 |
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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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| No log | 1.0 | 379 | 0.8877 | 0.5421 | 0.4232 | 0.4754 | 0.7697 | |
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| 0.8712 | 2.0 | 758 | 0.7159 | 0.5625 | 0.4761 | 0.5157 | 0.8265 | |
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| 0.1507 | 3.0 | 1137 | 0.4917 | 0.6528 | 0.5265 | 0.5829 | 0.8724 | |
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| 0.0984 | 4.0 | 1516 | 0.3969 | 0.7123 | 0.6516 | 0.6806 | 0.9005 | |
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| 0.0984 | 5.0 | 1895 | 0.3112 | 0.7463 | 0.6452 | 0.6920 | 0.9090 | |
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| 0.0732 | 6.0 | 2274 | 0.2653 | 0.8166 | 0.7239 | 0.7674 | 0.9299 | |
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| 0.0561 | 7.0 | 2653 | 0.2200 | 0.8006 | 0.7148 | 0.7553 | 0.9308 | |
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| 0.0465 | 8.0 | 3032 | 0.1590 | 0.8451 | 0.7884 | 0.8158 | 0.9485 | |
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| 0.0465 | 9.0 | 3411 | 0.1526 | 0.8568 | 0.8258 | 0.8410 | 0.9542 | |
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| 0.0396 | 10.0 | 3790 | 0.1494 | 0.8493 | 0.8142 | 0.8314 | 0.9526 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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