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---
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
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