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# bert-base-chinese-david-ner

This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0557
- Precision: 0.9424
- Recall: 0.9568
- F1: 0.9496
- Accuracy: 0.9890

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.0617        | 0.1   | 100  | 0.4293          | 0.2681    | 0.2160 | 0.2393 | 0.8405   |
| 0.2546        | 0.2   | 200  | 0.1427          | 0.7154    | 0.8018 | 0.7561 | 0.9523   |
| 0.1644        | 0.3   | 300  | 0.1148          | 0.7712    | 0.8437 | 0.8058 | 0.9628   |
| 0.132         | 0.39  | 400  | 0.0945          | 0.7956    | 0.8704 | 0.8313 | 0.9691   |
| 0.107         | 0.49  | 500  | 0.0839          | 0.8425    | 0.8971 | 0.8689 | 0.9747   |
| 0.0981        | 0.59  | 600  | 0.0971          | 0.8539    | 0.9060 | 0.8792 | 0.9733   |
| 0.098         | 0.69  | 700  | 0.0794          | 0.8832    | 0.9034 | 0.8932 | 0.9777   |
| 0.0955        | 0.79  | 800  | 0.0716          | 0.9012    | 0.9276 | 0.9142 | 0.9821   |
| 0.0824        | 0.89  | 900  | 0.0697          | 0.8848    | 0.9276 | 0.9057 | 0.9789   |
| 0.0774        | 0.99  | 1000 | 0.0631          | 0.8929    | 0.9212 | 0.9068 | 0.9808   |
| 0.0604        | 1.09  | 1100 | 0.0701          | 0.9087    | 0.9238 | 0.9162 | 0.9812   |
| 0.0621        | 1.18  | 1200 | 0.0583          | 0.9126    | 0.9288 | 0.9207 | 0.9841   |
| 0.0446        | 1.28  | 1300 | 0.0652          | 0.9175    | 0.9327 | 0.9250 | 0.9839   |
| 0.0516        | 1.38  | 1400 | 0.0609          | 0.9093    | 0.9301 | 0.9196 | 0.9842   |
| 0.0539        | 1.48  | 1500 | 0.0648          | 0.9179    | 0.9377 | 0.9277 | 0.9858   |
| 0.0546        | 1.58  | 1600 | 0.0676          | 0.9157    | 0.9390 | 0.9272 | 0.9825   |
| 0.0479        | 1.68  | 1700 | 0.0574          | 0.9106    | 0.9314 | 0.9209 | 0.9848   |
| 0.0424        | 1.78  | 1800 | 0.0572          | 0.9228    | 0.9416 | 0.9321 | 0.9862   |
| 0.054         | 1.88  | 1900 | 0.0499          | 0.9195    | 0.9428 | 0.9310 | 0.9866   |
| 0.0397        | 1.97  | 2000 | 0.0542          | 0.9318    | 0.9555 | 0.9435 | 0.9876   |
| 0.0362        | 2.07  | 2100 | 0.0567          | 0.9217    | 0.9428 | 0.9322 | 0.9867   |
| 0.0226        | 2.17  | 2200 | 0.0670          | 0.925     | 0.9403 | 0.9326 | 0.9854   |
| 0.029         | 2.27  | 2300 | 0.0565          | 0.9375    | 0.9530 | 0.9452 | 0.9883   |
| 0.0293        | 2.37  | 2400 | 0.0540          | 0.9254    | 0.9454 | 0.9353 | 0.9866   |
| 0.0265        | 2.47  | 2500 | 0.0551          | 0.9304    | 0.9517 | 0.9410 | 0.9880   |
| 0.0244        | 2.57  | 2600 | 0.0543          | 0.9316    | 0.9517 | 0.9415 | 0.9886   |
| 0.027         | 2.67  | 2700 | 0.0500          | 0.9399    | 0.9543 | 0.9470 | 0.9894   |
| 0.0286        | 2.76  | 2800 | 0.0479          | 0.9282    | 0.9530 | 0.9404 | 0.9890   |
| 0.0206        | 2.86  | 2900 | 0.0549          | 0.9255    | 0.9466 | 0.9359 | 0.9880   |
| 0.0239        | 2.96  | 3000 | 0.0537          | 0.9294    | 0.9530 | 0.9410 | 0.9889   |
| 0.0178        | 3.06  | 3100 | 0.0557          | 0.9424    | 0.9568 | 0.9496 | 0.9890   |
| 0.0131        | 3.16  | 3200 | 0.0627          | 0.9327    | 0.9504 | 0.9415 | 0.9880   |
| 0.0161        | 3.26  | 3300 | 0.0586          | 0.9340    | 0.9530 | 0.9434 | 0.9883   |
| 0.0162        | 3.36  | 3400 | 0.0542          | 0.9303    | 0.9504 | 0.9403 | 0.9887   |
| 0.0212        | 3.46  | 3500 | 0.0562          | 0.9268    | 0.9492 | 0.9379 | 0.9881   |
| 0.02          | 3.55  | 3600 | 0.0551          | 0.9280    | 0.9504 | 0.9391 | 0.9888   |
| 0.0084        | 3.65  | 3700 | 0.0568          | 0.9292    | 0.9504 | 0.9397 | 0.9888   |
| 0.0143        | 3.75  | 3800 | 0.0564          | 0.9363    | 0.9530 | 0.9446 | 0.9892   |
| 0.0162        | 3.85  | 3900 | 0.0560          | 0.9377    | 0.9568 | 0.9472 | 0.9888   |
| 0.0199        | 3.95  | 4000 | 0.0546          | 0.9377    | 0.9568 | 0.9472 | 0.9894   |


### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 2.11.0
- Tokenizers 0.13.3

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

bert-base-chinese-david-ner
===========================


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


* Loss: 0.0557
* Precision: 0.9424
* Recall: 0.9568
* F1: 0.9496
* Accuracy: 0.9890


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 1.10.1+cu113
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 1.10.1+cu113
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 1.10.1+cu113
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 4### Training results### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 1.10.1+cu113
* Datasets 2.11.0
* Tokenizers 0.13.3