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# bert-base-chinese-finetuned-ner_0220_J_ORIDATA_FULL_NOMOD

This model is a fine-tuned version of [ckiplab/bert-base-chinese-ner](https://huggingface.co/ckiplab/bert-base-chinese-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0522
- Precision: 0.9728
- Recall: 0.9739
- F1: 0.9733
- Accuracy: 0.9954

## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3616        | 1.0   | 705  | 0.0914          | 0.8789    | 0.9239 | 0.9008 | 0.9821   |
| 0.0643        | 2.0   | 1410 | 0.0602          | 0.9242    | 0.9420 | 0.9330 | 0.9912   |
| 0.0339        | 3.0   | 2115 | 0.0533          | 0.9385    | 0.9545 | 0.9465 | 0.9910   |
| 0.024         | 4.0   | 2820 | 0.0558          | 0.9595    | 0.9693 | 0.9644 | 0.9932   |
| 0.0145        | 5.0   | 3525 | 0.0584          | 0.9484    | 0.9614 | 0.9549 | 0.9921   |
| 0.007         | 6.0   | 4230 | 0.0535          | 0.9637    | 0.9648 | 0.9642 | 0.9940   |
| 0.0145        | 7.0   | 4935 | 0.0492          | 0.9573    | 0.9682 | 0.9627 | 0.9942   |
| 0.0091        | 8.0   | 5640 | 0.0486          | 0.9694    | 0.9716 | 0.9705 | 0.9957   |
| 0.0049        | 9.0   | 6345 | 0.0526          | 0.9727    | 0.9727 | 0.9727 | 0.9950   |
| 0.0033        | 10.0  | 7050 | 0.0515          | 0.9661    | 0.9727 | 0.9694 | 0.9949   |
| 0.0023        | 11.0  | 7755 | 0.0523          | 0.9661    | 0.9716 | 0.9688 | 0.9950   |
| 0.0019        | 12.0  | 8460 | 0.0522          | 0.9728    | 0.9739 | 0.9733 | 0.9954   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.13.0+cu117
- Datasets 2.8.0
- Tokenizers 0.12.1

{"license": "gpl-3.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-chinese-finetuned-ner_0220_J_ORIDATA_FULL_NOMOD", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer license:gpl-3.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-base-chinese-finetuned-ner\_0220\_J\_ORIDATA\_FULL\_NOMOD
==============================================================


This model is a fine-tuned version of ckiplab/bert-base-chinese-ner on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0522
* Precision: 0.9728
* Recall: 0.9739
* F1: 0.9733
* Accuracy: 0.9954


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: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.13.0+cu117
* Datasets 2.8.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.13.0+cu117
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.13.0+cu117
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.13.0+cu117
* Datasets 2.8.0
* Tokenizers 0.12.1