
<!-- 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-v3

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: 0.0498
- Precision: 0.9984
- Recall: 0.9991
- F1-score: 0.9988

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|
| 0.0862        | 1.0   | 848  | 0.0434          | 0.9940    | 0.9991 | 0.9965   |
| 0.0349        | 2.0   | 1696 | 0.0370          | 0.9980    | 0.9980 | 0.9980   |
| 0.0222        | 3.0   | 2544 | 0.0395          | 0.9987    | 0.9991 | 0.9989   |
| 0.0142        | 4.0   | 3392 | 0.0414          | 0.9982    | 0.9991 | 0.9987   |
| 0.0083        | 5.0   | 4240 | 0.0498          | 0.9984    | 0.9991 | 0.9988   |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-ner-ja-v3", "results": []}]}
transformers safetensors xlm-roberta token-classification generated_from_trainer base_model:xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us