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# bert-base-chinese-wikiann-zh-ner

This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2092
- Precision: 0.7891
- Recall: 0.8061
- F1: 0.7975
- Accuracy: 0.9432

## 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: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.842         | 0.16  | 400  | 0.3530          | 0.5535    | 0.6872 | 0.6131 | 0.8927   |
| 0.32          | 0.32  | 800  | 0.2800          | 0.6929    | 0.6749 | 0.6838 | 0.9190   |
| 0.2928        | 0.48  | 1200 | 0.2438          | 0.7031    | 0.7661 | 0.7333 | 0.9301   |
| 0.245         | 0.64  | 1600 | 0.2525          | 0.6959    | 0.7919 | 0.7408 | 0.9280   |
| 0.2236        | 0.8   | 2000 | 0.2315          | 0.7441    | 0.7503 | 0.7472 | 0.9342   |
| 0.2444        | 0.96  | 2400 | 0.2119          | 0.7719    | 0.7675 | 0.7697 | 0.9379   |
| 0.1899        | 1.12  | 2800 | 0.2267          | 0.7531    | 0.8062 | 0.7788 | 0.9387   |
| 0.1649        | 1.28  | 3200 | 0.2249          | 0.7519    | 0.8202 | 0.7846 | 0.9395   |
| 0.1521        | 1.44  | 3600 | 0.2220          | 0.7778    | 0.8032 | 0.7903 | 0.9413   |
| 0.1787        | 1.6   | 4000 | 0.2185          | 0.7879    | 0.7860 | 0.7869 | 0.9417   |
| 0.146         | 1.76  | 4400 | 0.2134          | 0.7721    | 0.8128 | 0.7919 | 0.9416   |
| 0.1557        | 1.92  | 4800 | 0.2111          | 0.7857    | 0.8101 | 0.7977 | 0.9429   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-chinese-wikiann-zh-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "config": "zh", "split": "validation", "args": "zh"}, "metrics": [{"type": "precision", "value": 0.7890612756621219, "name": "Precision"}, {"type": "recall", "value": 0.8060513887777155, "name": "Recall"}, {"type": "f1", "value": 0.797465848346862, "name": "F1"}, {"type": "accuracy", "value": 0.9432393178410795, "name": "Accuracy"}]}]}]}
transformers pytorch bert token-classification generated_from_trainer dataset:wikiann model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-wikiann #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-base-chinese-wikiann-zh-ner
================================


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


* Loss: 0.2092
* Precision: 0.7891
* Recall: 0.8061
* F1: 0.7975
* Accuracy: 0.9432


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: 2
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* 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: 2
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-wikiann #model-index #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: 2
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-wikiann #model-index #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: 2
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3