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---
library_name: transformers
base_model: FacebookAI/xlm-roberta-large-finetuned-conll03-english
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-finetuned-conll03-english-finetuned-ner-biomedical-spanish
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-finetuned-conll03-english-finetuned-ner-biomedical-spanish
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.
It achieves the following results on the evaluation set:
- Loss: 0.1526
- Precision: 0.8568
- Recall: 0.8258
- F1: 0.8410
- Accuracy: 0.9542
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 379 | 0.8877 | 0.5421 | 0.4232 | 0.4754 | 0.7697 |
| 0.8712 | 2.0 | 758 | 0.7159 | 0.5625 | 0.4761 | 0.5157 | 0.8265 |
| 0.1507 | 3.0 | 1137 | 0.4917 | 0.6528 | 0.5265 | 0.5829 | 0.8724 |
| 0.0984 | 4.0 | 1516 | 0.3969 | 0.7123 | 0.6516 | 0.6806 | 0.9005 |
| 0.0984 | 5.0 | 1895 | 0.3112 | 0.7463 | 0.6452 | 0.6920 | 0.9090 |
| 0.0732 | 6.0 | 2274 | 0.2653 | 0.8166 | 0.7239 | 0.7674 | 0.9299 |
| 0.0561 | 7.0 | 2653 | 0.2200 | 0.8006 | 0.7148 | 0.7553 | 0.9308 |
| 0.0465 | 8.0 | 3032 | 0.1590 | 0.8451 | 0.7884 | 0.8158 | 0.9485 |
| 0.0465 | 9.0 | 3411 | 0.1526 | 0.8568 | 0.8258 | 0.8410 | 0.9542 |
| 0.0396 | 10.0 | 3790 | 0.1494 | 0.8493 | 0.8142 | 0.8314 | 0.9526 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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