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# Cbert_base_ws-finetuned-ner

This model is a fine-tuned version of [ckiplab/bert-base-chinese-ws](https://huggingface.co/ckiplab/bert-base-chinese-ws) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0582
- Precision: 0.9602
- Recall: 0.9633
- F1: 0.9617
- Accuracy: 0.9827

## 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: 18
- eval_batch_size: 18
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0482        | 0.64  | 1000  | 0.0509          | 0.9601    | 0.9582 | 0.9592 | 0.9817   |
| 0.0364        | 1.28  | 2000  | 0.0521          | 0.9590    | 0.9615 | 0.9602 | 0.9820   |
| 0.0341        | 1.92  | 3000  | 0.0548          | 0.9546    | 0.9625 | 0.9585 | 0.9812   |
| 0.0264        | 2.56  | 4000  | 0.0550          | 0.9593    | 0.9623 | 0.9608 | 0.9822   |
| 0.0227        | 3.19  | 5000  | 0.0582          | 0.9602    | 0.9633 | 0.9617 | 0.9827   |
| 0.021         | 3.83  | 6000  | 0.0595          | 0.9581    | 0.9624 | 0.9603 | 0.9820   |
| 0.0162        | 4.47  | 7000  | 0.0686          | 0.9574    | 0.9626 | 0.9600 | 0.9819   |
| 0.0159        | 5.11  | 8000  | 0.0719          | 0.9596    | 0.9614 | 0.9605 | 0.9822   |
| 0.0144        | 5.75  | 9000  | 0.0732          | 0.9590    | 0.9620 | 0.9605 | 0.9822   |
| 0.0109        | 6.39  | 10000 | 0.0782          | 0.9599    | 0.9626 | 0.9612 | 0.9824   |
| 0.0122        | 7.03  | 11000 | 0.0803          | 0.9605    | 0.9620 | 0.9612 | 0.9825   |
| 0.0097        | 7.67  | 12000 | 0.0860          | 0.9591    | 0.9620 | 0.9605 | 0.9822   |
| 0.0087        | 8.31  | 13000 | 0.0877          | 0.9591    | 0.9616 | 0.9603 | 0.9821   |
| 0.0087        | 8.95  | 14000 | 0.0902          | 0.9585    | 0.9630 | 0.9607 | 0.9823   |
| 0.0078        | 9.58  | 15000 | 0.0929          | 0.9589    | 0.9621 | 0.9605 | 0.9821   |


### Framework versions

- Transformers 4.13.0
- Pytorch 1.8.0+cu111
- Datasets 2.4.0
- Tokenizers 0.10.3

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

Cbert\_base\_ws-finetuned-ner
=============================


This model is a fine-tuned version of ckiplab/bert-base-chinese-ws on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0582
* Precision: 0.9602
* Recall: 0.9633
* F1: 0.9617
* Accuracy: 0.9827


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: 18
* eval\_batch\_size: 18
* 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 4.13.0
* Pytorch 1.8.0+cu111
* Datasets 2.4.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 18
* eval\_batch\_size: 18
* 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 4.13.0
* Pytorch 1.8.0+cu111
* Datasets 2.4.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #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: 18
* eval\_batch\_size: 18
* 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 4.13.0
* Pytorch 1.8.0+cu111
* Datasets 2.4.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #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: 18
* eval\_batch\_size: 18
* 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 4.13.0
* Pytorch 1.8.0+cu111
* Datasets 2.4.0
* Tokenizers 0.10.3