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# bert-japanese-ner

This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-v3](https://huggingface.co/cl-tohoku/bert-base-japanese-v3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0372
- Precision: 0.9673
- Recall: 0.9682
- F1: 0.9678
- Accuracy: 0.9933

## 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     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0553        | 1.0   | 848  | 0.0263          | 0.9683    | 0.9334 | 0.9505 | 0.9908   |
| 0.0133        | 2.0   | 1696 | 0.0241          | 0.9707    | 0.9560 | 0.9633 | 0.9928   |
| 0.0065        | 3.0   | 2544 | 0.0245          | 0.9631    | 0.9706 | 0.9668 | 0.9935   |
| 0.0027        | 4.0   | 3392 | 0.0321          | 0.9716    | 0.9659 | 0.9687 | 0.9936   |
| 0.0012        | 5.0   | 4240 | 0.0372          | 0.9673    | 0.9682 | 0.9678 | 0.9933   |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "cl-tohoku/bert-base-japanese-v3", "model-index": [{"name": "bert-japanese-ner", "results": []}]}
transformers safetensors bert token-classification generated_from_trainer base_model:cl-tohoku/bert-base-japanese-v3 license:apache-2.0 autotrain_compatible endpoints_compatible region:us