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# bert-base-chinese-finetuned-ner_0301_J_DATA

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: 0.0764
- Precision: 0.9663
- Recall: 0.9708
- F1: 0.9685
- Accuracy: 0.9925

## 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: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3872        | 1.0   | 705   | 0.1222          | 0.9088    | 0.9311 | 0.9198 | 0.9781   |
| 0.0732        | 2.0   | 1410  | 0.0642          | 0.9303    | 0.9509 | 0.9405 | 0.9900   |
| 0.034         | 3.0   | 2115  | 0.0588          | 0.9616    | 0.9661 | 0.9639 | 0.9909   |
| 0.0267        | 4.0   | 2820  | 0.0631          | 0.9639    | 0.9673 | 0.9656 | 0.9925   |
| 0.0232        | 5.0   | 3525  | 0.0617          | 0.9630    | 0.9720 | 0.9674 | 0.9924   |
| 0.017         | 6.0   | 4230  | 0.0652          | 0.9674    | 0.9708 | 0.9691 | 0.9926   |
| 0.0123        | 7.0   | 4935  | 0.0573          | 0.9618    | 0.9720 | 0.9669 | 0.9923   |
| 0.009         | 8.0   | 5640  | 0.0667          | 0.9651    | 0.9696 | 0.9674 | 0.9922   |
| 0.0055        | 9.0   | 6345  | 0.0768          | 0.9640    | 0.9696 | 0.9668 | 0.9925   |
| 0.0045        | 10.0  | 7050  | 0.0775          | 0.9662    | 0.9696 | 0.9679 | 0.9925   |
| 0.004         | 11.0  | 7755  | 0.0753          | 0.9606    | 0.9685 | 0.9645 | 0.9923   |
| 0.0018        | 12.0  | 8460  | 0.0735          | 0.9629    | 0.9696 | 0.9662 | 0.9925   |
| 0.0019        | 13.0  | 9165  | 0.0754          | 0.9663    | 0.9708 | 0.9685 | 0.9927   |
| 0.0019        | 14.0  | 9870  | 0.0760          | 0.9651    | 0.9696 | 0.9674 | 0.9925   |
| 0.0013        | 15.0  | 10575 | 0.0764          | 0.9663    | 0.9708 | 0.9685 | 0.9925   |


### 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_0301_J_DATA", "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\_0301\_J\_DATA
==============================================


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: 0.0764
* Precision: 0.9663
* Recall: 0.9708
* F1: 0.9685
* Accuracy: 0.9925


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: 15


### 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: 15 ### 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: 15 ### 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: 15### Training results### Framework versions


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