---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
base_model: xlm-roberta-base
model-index:
- name: xlm-roberta-base-finetuned-pos
  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-base-finetuned-pos

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0743
- Precision: 0.9800
- Recall: 0.9811
- F1: 0.9806
- Accuracy: 0.9817

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.157         | 1.0   | 1583 | 0.1170          | 0.9616    | 0.9641 | 0.9629 | 0.9671   |
| 0.0982        | 2.0   | 3166 | 0.0823          | 0.9740    | 0.9745 | 0.9743 | 0.9766   |
| 0.0666        | 3.0   | 4749 | 0.0778          | 0.9767    | 0.9771 | 0.9769 | 0.9786   |
| 0.0528        | 4.0   | 6332 | 0.0712          | 0.9793    | 0.9800 | 0.9796 | 0.9810   |
| 0.0446        | 5.0   | 7915 | 0.0743          | 0.9800    | 0.9811 | 0.9806 | 0.9817   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3