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# bert-japanese-ner

This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-whole-word-masking](https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0842

## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2834        | 1.0   | 179  | 0.0915          |
| 0.0548        | 2.0   | 358  | 0.0831          |
| 0.0235        | 3.0   | 537  | 0.0842          |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.12.1.post201
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-japanese-ner", "results": []}]}
transformers pytorch bert token-classification generated_from_trainer license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-japanese-ner
=================


This model is a fine-tuned version of cl-tohoku/bert-base-japanese-whole-word-masking on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0842


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: 24
* eval\_batch\_size: 24
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 1.12.1.post201
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 24
* eval\_batch\_size: 24
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.12.1.post201
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 24
* eval\_batch\_size: 24
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.12.1.post201
* Datasets 2.12.0
* Tokenizers 0.13.3