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

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: 0.2085
- Precision: 0.7834
- Recall: 0.8069
- F1: 0.7950
- Accuracy: 0.9435

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.773         | 0.16  | 400  | 0.3232          | 0.5816    | 0.6670 | 0.6214 | 0.9049   |
| 0.3149        | 0.32  | 800  | 0.2954          | 0.6832    | 0.6923 | 0.6877 | 0.9195   |
| 0.2912        | 0.48  | 1200 | 0.2418          | 0.7010    | 0.7551 | 0.7270 | 0.9299   |
| 0.2446        | 0.64  | 1600 | 0.2539          | 0.7159    | 0.7743 | 0.7440 | 0.9292   |
| 0.2193        | 0.8   | 2000 | 0.2330          | 0.7441    | 0.7613 | 0.7526 | 0.9351   |
| 0.2434        | 0.96  | 2400 | 0.2186          | 0.7603    | 0.7696 | 0.7649 | 0.9369   |
| 0.1915        | 1.12  | 2800 | 0.2245          | 0.7568    | 0.8032 | 0.7793 | 0.9398   |
| 0.1607        | 1.28  | 3200 | 0.2263          | 0.7566    | 0.8138 | 0.7842 | 0.9399   |
| 0.1513        | 1.44  | 3600 | 0.2228          | 0.7782    | 0.7964 | 0.7872 | 0.9414   |
| 0.1777        | 1.6   | 4000 | 0.2098          | 0.7857    | 0.7916 | 0.7887 | 0.9423   |
| 0.1466        | 1.76  | 4400 | 0.2132          | 0.7673    | 0.8163 | 0.7911 | 0.9418   |
| 0.1528        | 1.92  | 4800 | 0.2093          | 0.7793    | 0.8114 | 0.7951 | 0.9435   |


### 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-new", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "config": "zh", "split": "validation", "args": "zh"}, "metrics": [{"type": "precision", "value": 0.7833553500660502, "name": "Precision"}, {"type": "recall", "value": 0.8069318818538381, "name": "Recall"}, {"type": "f1", "value": 0.7949688510369846, "name": "F1"}, {"type": "accuracy", "value": 0.9435204272863568, "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-new
====================================


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: 0.2085
* Precision: 0.7834
* Recall: 0.8069
* F1: 0.7950
* Accuracy: 0.9435


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