---
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