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# bert-base-chinese-finetuned-ner

This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the fdner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1016
- Precision: 0.9146
- Recall: 0.9414
- F1: 0.9278
- Accuracy: 0.9751

## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 2    | 0.9181          | 0.1271    | 0.1255 | 0.1263 | 0.7170   |
| No log        | 2.0   | 4    | 0.8048          | 0.1919    | 0.2385 | 0.2127 | 0.7669   |
| No log        | 3.0   | 6    | 0.7079          | 0.2422    | 0.3264 | 0.2781 | 0.7980   |
| No log        | 4.0   | 8    | 0.6201          | 0.3505    | 0.4854 | 0.4070 | 0.8338   |
| No log        | 5.0   | 10   | 0.5462          | 0.3898    | 0.4812 | 0.4307 | 0.8611   |
| No log        | 6.0   | 12   | 0.4851          | 0.4749    | 0.5941 | 0.5279 | 0.8802   |
| No log        | 7.0   | 14   | 0.4338          | 0.5213    | 0.6151 | 0.5643 | 0.8936   |
| No log        | 8.0   | 16   | 0.3843          | 0.5663    | 0.6611 | 0.6100 | 0.9076   |
| No log        | 9.0   | 18   | 0.3451          | 0.6255    | 0.6987 | 0.6601 | 0.9214   |
| No log        | 10.0  | 20   | 0.3058          | 0.6719    | 0.7197 | 0.6949 | 0.9293   |
| No log        | 11.0  | 22   | 0.2783          | 0.6808    | 0.7406 | 0.7094 | 0.9344   |
| No log        | 12.0  | 24   | 0.2497          | 0.7050    | 0.7699 | 0.7360 | 0.9427   |
| No log        | 13.0  | 26   | 0.2235          | 0.7519    | 0.8117 | 0.7807 | 0.9506   |
| No log        | 14.0  | 28   | 0.2031          | 0.7713    | 0.8326 | 0.8008 | 0.9552   |
| No log        | 15.0  | 30   | 0.1861          | 0.7915    | 0.8577 | 0.8233 | 0.9593   |
| No log        | 16.0  | 32   | 0.1726          | 0.8031    | 0.8703 | 0.8353 | 0.9613   |
| No log        | 17.0  | 34   | 0.1619          | 0.8320    | 0.8912 | 0.8606 | 0.9641   |
| No log        | 18.0  | 36   | 0.1521          | 0.8571    | 0.9038 | 0.8798 | 0.9674   |
| No log        | 19.0  | 38   | 0.1420          | 0.8710    | 0.9038 | 0.8871 | 0.9695   |
| No log        | 20.0  | 40   | 0.1352          | 0.8795    | 0.9163 | 0.8975 | 0.9700   |
| No log        | 21.0  | 42   | 0.1281          | 0.8755    | 0.9121 | 0.8934 | 0.9712   |
| No log        | 22.0  | 44   | 0.1209          | 0.8916    | 0.9289 | 0.9098 | 0.9728   |
| No log        | 23.0  | 46   | 0.1155          | 0.8924    | 0.9372 | 0.9143 | 0.9733   |
| No log        | 24.0  | 48   | 0.1115          | 0.904     | 0.9456 | 0.9243 | 0.9746   |
| No log        | 25.0  | 50   | 0.1087          | 0.9116    | 0.9498 | 0.9303 | 0.9746   |
| No log        | 26.0  | 52   | 0.1068          | 0.9146    | 0.9414 | 0.9278 | 0.9740   |
| No log        | 27.0  | 54   | 0.1054          | 0.9146    | 0.9414 | 0.9278 | 0.9743   |
| No log        | 28.0  | 56   | 0.1036          | 0.9146    | 0.9414 | 0.9278 | 0.9743   |
| No log        | 29.0  | 58   | 0.1022          | 0.9146    | 0.9414 | 0.9278 | 0.9746   |
| No log        | 30.0  | 60   | 0.1016          | 0.9146    | 0.9414 | 0.9278 | 0.9751   |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "datasets": ["fdner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-chinese-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "fdner", "type": "fdner", "args": "fdner"}, "metrics": [{"type": "precision", "value": 0.9146341463414634, "name": "Precision"}, {"type": "recall", "value": 0.9414225941422594, "name": "Recall"}, {"type": "f1", "value": 0.9278350515463917, "name": "F1"}, {"type": "accuracy", "value": 0.9750636132315522, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:fdner model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-fdner #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-base-chinese-finetuned-ner
===============================


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


* Loss: 0.1016
* Precision: 0.9146
* Recall: 0.9414
* F1: 0.9278
* Accuracy: 0.9751


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: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-fdner #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-fdner #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30### Training results### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0