---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: xlm-roberta-ner-ja-v4
  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-v4

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: 0.0615
- Precision: 0.9955
- Recall: 0.9978
- F1-score: 0.9966

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|
| 0.0884        | 1.0   | 778  | 0.0488          | 0.9820    | 0.9838 | 0.9829   |
| 0.0403        | 2.0   | 1556 | 0.0460          | 0.9888    | 0.9924 | 0.9906   |
| 0.0256        | 3.0   | 2334 | 0.0518          | 0.9910    | 0.9928 | 0.9919   |
| 0.0162        | 4.0   | 3112 | 0.0523          | 0.9951    | 0.9973 | 0.9962   |
| 0.0087        | 5.0   | 3890 | 0.0615          | 0.9955    | 0.9978 | 0.9966   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0