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# bert-base-chinese-finetuned-ner_0220_J_ORIDATA

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: 0.4109
- Precision: 0.9088
- Recall: 0.9581
- F1: 0.9328
- Accuracy: 0.9478

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5095        | 1.0   | 884  | 0.2940          | 0.8565    | 0.9269 | 0.8903 | 0.9355   |
| 0.2381        | 2.0   | 1768 | 0.2669          | 0.8910    | 0.9474 | 0.9184 | 0.9442   |
| 0.2057        | 3.0   | 2652 | 0.2566          | 0.9011    | 0.9507 | 0.9252 | 0.9438   |
| 0.1856        | 4.0   | 3536 | 0.2811          | 0.9053    | 0.9507 | 0.9275 | 0.9414   |
| 0.1386        | 5.0   | 4420 | 0.3108          | 0.9019    | 0.9523 | 0.9265 | 0.9481   |
| 0.1224        | 6.0   | 5304 | 0.3265          | 0.8978    | 0.9532 | 0.9247 | 0.9430   |
| 0.0891        | 7.0   | 6188 | 0.3601          | 0.9071    | 0.9548 | 0.9303 | 0.9471   |
| 0.08          | 8.0   | 7072 | 0.3555          | 0.8931    | 0.9540 | 0.9225 | 0.9458   |
| 0.0547        | 9.0   | 7956 | 0.4065          | 0.9089    | 0.9589 | 0.9332 | 0.9482   |
| 0.0539        | 10.0  | 8840 | 0.4109          | 0.9088    | 0.9581 | 0.9328 | 0.9478   |


### 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", "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
=================================================


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: 0.4109
* Precision: 0.9088
* Recall: 0.9581
* F1: 0.9328
* Accuracy: 0.9478


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: 10


### 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: 10 ### 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: 10 ### 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: 10### Training results### Framework versions


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