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---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xmlNer-biobert
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. -->
# xmlNer-biobert
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0843
- Precision: 0.9481
- Recall: 0.9752
- F1: 0.9615
- Accuracy: 0.9816
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4975 | 1.0 | 612 | 0.1100 | 0.9268 | 0.9546 | 0.9405 | 0.9713 |
| 0.1263 | 2.0 | 1224 | 0.0902 | 0.9359 | 0.9720 | 0.9536 | 0.9776 |
| 0.0894 | 3.0 | 1836 | 0.0861 | 0.9437 | 0.9725 | 0.9579 | 0.9790 |
| 0.0687 | 4.0 | 2448 | 0.0841 | 0.9461 | 0.9748 | 0.9603 | 0.9809 |
| 0.0497 | 5.0 | 3060 | 0.0843 | 0.9481 | 0.9752 | 0.9615 | 0.9816 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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