---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: xlm-roberta-ner-ja-v5
  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-ner-ja-v5

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: 0.0556
- Precision: 0.9131
- Recall: 0.9879
- F1-score: 0.9490

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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-score |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|
| 0.0888        | 1.0   | 837  | 0.0424          | 0.9014    | 0.9697 | 0.9343   |
| 0.0438        | 2.0   | 1674 | 0.0428          | 0.8647    | 0.9851 | 0.9210   |
| 0.0293        | 3.0   | 2511 | 0.0467          | 0.8746    | 0.9713 | 0.9205   |
| 0.0185        | 4.0   | 3348 | 0.0484          | 0.8707    | 0.9758 | 0.9203   |
| 0.0117        | 5.0   | 4185 | 0.0556          | 0.9131    | 0.9879 | 0.9490   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.0.1+cu118
- Datasets 2.17.1
- Tokenizers 0.15.2