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# test

This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7372
- Precision: 0.7696
- Recall: 0.8396
- F1: 0.8031
- Accuracy: 0.8847

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 2    | 1.9496          | 0.0       | 0.0    | 0.0    | 0.4889   |
| No log        | 2.0   | 4    | 1.6137          | 0.0       | 0.0    | 0.0    | 0.4919   |
| No log        | 3.0   | 6    | 1.3906          | 0.0       | 0.0    | 0.0    | 0.5650   |
| No log        | 4.0   | 8    | 1.2273          | 0.0652    | 0.0481 | 0.0554 | 0.6856   |
| No log        | 5.0   | 10   | 1.0565          | 0.2051    | 0.1711 | 0.1866 | 0.7125   |
| No log        | 6.0   | 12   | 0.9150          | 0.5094    | 0.4332 | 0.4682 | 0.7540   |
| No log        | 7.0   | 14   | 0.8051          | 0.5988    | 0.5187 | 0.5559 | 0.7679   |
| No log        | 8.0   | 16   | 0.7151          | 0.6707    | 0.5989 | 0.6328 | 0.7763   |
| No log        | 9.0   | 18   | 0.6334          | 0.6685    | 0.6364 | 0.6521 | 0.8086   |
| No log        | 10.0  | 20   | 0.5693          | 0.6957    | 0.6845 | 0.6900 | 0.8201   |
| No log        | 11.0  | 22   | 0.5192          | 0.7166    | 0.7166 | 0.7166 | 0.8363   |
| No log        | 12.0  | 24   | 0.4736          | 0.7135    | 0.7326 | 0.7230 | 0.8524   |
| No log        | 13.0  | 26   | 0.4448          | 0.6938    | 0.7754 | 0.7323 | 0.8555   |
| No log        | 14.0  | 28   | 0.4280          | 0.7177    | 0.8021 | 0.7576 | 0.8586   |
| No log        | 15.0  | 30   | 0.4179          | 0.7588    | 0.8075 | 0.7824 | 0.8663   |
| No log        | 16.0  | 32   | 0.4214          | 0.7356    | 0.8182 | 0.7747 | 0.8593   |
| No log        | 17.0  | 34   | 0.4070          | 0.7391    | 0.8182 | 0.7766 | 0.8616   |
| No log        | 18.0  | 36   | 0.4112          | 0.7586    | 0.8235 | 0.7897 | 0.8724   |
| No log        | 19.0  | 38   | 0.4530          | 0.7330    | 0.8075 | 0.7684 | 0.8693   |
| No log        | 20.0  | 40   | 0.4719          | 0.7766    | 0.8182 | 0.7969 | 0.8732   |
| No log        | 21.0  | 42   | 0.4886          | 0.7260    | 0.8075 | 0.7646 | 0.8632   |
| No log        | 22.0  | 44   | 0.5007          | 0.7217    | 0.8182 | 0.7669 | 0.8701   |
| No log        | 23.0  | 46   | 0.5169          | 0.7321    | 0.8182 | 0.7727 | 0.8762   |
| No log        | 24.0  | 48   | 0.5531          | 0.7238    | 0.8128 | 0.7657 | 0.8724   |
| No log        | 25.0  | 50   | 0.5895          | 0.7311    | 0.8289 | 0.7769 | 0.8655   |
| No log        | 26.0  | 52   | 0.5482          | 0.7330    | 0.8075 | 0.7684 | 0.8778   |
| No log        | 27.0  | 54   | 0.5361          | 0.7488    | 0.8128 | 0.7795 | 0.8832   |
| No log        | 28.0  | 56   | 0.5378          | 0.7427    | 0.8182 | 0.7786 | 0.8847   |
| No log        | 29.0  | 58   | 0.5543          | 0.7371    | 0.8396 | 0.7850 | 0.8824   |
| No log        | 30.0  | 60   | 0.5564          | 0.7585    | 0.8396 | 0.7970 | 0.8839   |
| No log        | 31.0  | 62   | 0.5829          | 0.7235    | 0.8396 | 0.7772 | 0.8724   |
| No log        | 32.0  | 64   | 0.5974          | 0.7269    | 0.8396 | 0.7792 | 0.8716   |
| No log        | 33.0  | 66   | 0.5750          | 0.7610    | 0.8342 | 0.7959 | 0.8839   |
| No log        | 34.0  | 68   | 0.5887          | 0.7723    | 0.8342 | 0.8021 | 0.8878   |
| No log        | 35.0  | 70   | 0.6219          | 0.7441    | 0.8396 | 0.7889 | 0.8747   |
| No log        | 36.0  | 72   | 0.6676          | 0.7269    | 0.8396 | 0.7792 | 0.8632   |
| No log        | 37.0  | 74   | 0.6517          | 0.7452    | 0.8289 | 0.7848 | 0.8693   |
| No log        | 38.0  | 76   | 0.6346          | 0.7828    | 0.8289 | 0.8052 | 0.8862   |
| No log        | 39.0  | 78   | 0.6239          | 0.7839    | 0.8342 | 0.8083 | 0.8855   |
| No log        | 40.0  | 80   | 0.6360          | 0.7277    | 0.8289 | 0.775  | 0.8762   |
| No log        | 41.0  | 82   | 0.6645          | 0.7336    | 0.8396 | 0.7830 | 0.8701   |
| No log        | 42.0  | 84   | 0.6611          | 0.7406    | 0.8396 | 0.7870 | 0.8747   |
| No log        | 43.0  | 86   | 0.6707          | 0.7488    | 0.8289 | 0.7868 | 0.8762   |
| No log        | 44.0  | 88   | 0.6901          | 0.7277    | 0.8289 | 0.775  | 0.8709   |
| No log        | 45.0  | 90   | 0.6911          | 0.7393    | 0.8342 | 0.7839 | 0.8709   |
| No log        | 46.0  | 92   | 0.6540          | 0.7761    | 0.8342 | 0.8041 | 0.8878   |
| No log        | 47.0  | 94   | 0.6381          | 0.7761    | 0.8342 | 0.8041 | 0.8916   |
| No log        | 48.0  | 96   | 0.6285          | 0.7745    | 0.8449 | 0.8082 | 0.8885   |
| No log        | 49.0  | 98   | 0.6449          | 0.7692    | 0.8556 | 0.8101 | 0.8862   |
| No log        | 50.0  | 100  | 0.6809          | 0.7442    | 0.8556 | 0.7960 | 0.8732   |
| No log        | 51.0  | 102  | 0.6898          | 0.7395    | 0.8503 | 0.7910 | 0.8716   |
| No log        | 52.0  | 104  | 0.6897          | 0.75      | 0.8503 | 0.7970 | 0.8762   |
| No log        | 53.0  | 106  | 0.6714          | 0.7656    | 0.8556 | 0.8081 | 0.8855   |
| No log        | 54.0  | 108  | 0.6612          | 0.7692    | 0.8556 | 0.8101 | 0.8855   |
| No log        | 55.0  | 110  | 0.6583          | 0.7692    | 0.8556 | 0.8101 | 0.8855   |
| No log        | 56.0  | 112  | 0.6648          | 0.7692    | 0.8556 | 0.8101 | 0.8855   |
| No log        | 57.0  | 114  | 0.6757          | 0.7656    | 0.8556 | 0.8081 | 0.8832   |
| No log        | 58.0  | 116  | 0.6803          | 0.7656    | 0.8556 | 0.8081 | 0.8839   |
| No log        | 59.0  | 118  | 0.6834          | 0.7692    | 0.8556 | 0.8101 | 0.8862   |
| No log        | 60.0  | 120  | 0.6889          | 0.7833    | 0.8503 | 0.8154 | 0.8878   |
| No log        | 61.0  | 122  | 0.6963          | 0.7772    | 0.8396 | 0.8072 | 0.8862   |
| No log        | 62.0  | 124  | 0.7057          | 0.7772    | 0.8396 | 0.8072 | 0.8862   |
| No log        | 63.0  | 126  | 0.7212          | 0.7910    | 0.8503 | 0.8196 | 0.8862   |
| No log        | 64.0  | 128  | 0.7334          | 0.7833    | 0.8503 | 0.8154 | 0.8824   |
| No log        | 65.0  | 130  | 0.7398          | 0.7833    | 0.8503 | 0.8154 | 0.8801   |
| No log        | 66.0  | 132  | 0.7400          | 0.7833    | 0.8503 | 0.8154 | 0.8809   |
| No log        | 67.0  | 134  | 0.7345          | 0.7783    | 0.8449 | 0.8103 | 0.8855   |
| No log        | 68.0  | 136  | 0.7270          | 0.79      | 0.8449 | 0.8165 | 0.8870   |
| No log        | 69.0  | 138  | 0.7245          | 0.7839    | 0.8342 | 0.8083 | 0.8862   |
| No log        | 70.0  | 140  | 0.7260          | 0.7868    | 0.8289 | 0.8073 | 0.8847   |
| No log        | 71.0  | 142  | 0.7275          | 0.7817    | 0.8235 | 0.8021 | 0.8839   |
| No log        | 72.0  | 144  | 0.7283          | 0.7778    | 0.8235 | 0.8000 | 0.8832   |
| No log        | 73.0  | 146  | 0.7296          | 0.78      | 0.8342 | 0.8062 | 0.8847   |
| No log        | 74.0  | 148  | 0.7344          | 0.7734    | 0.8396 | 0.8051 | 0.8832   |
| No log        | 75.0  | 150  | 0.7314          | 0.7745    | 0.8449 | 0.8082 | 0.8824   |
| No log        | 76.0  | 152  | 0.7299          | 0.7794    | 0.8503 | 0.8133 | 0.8832   |
| No log        | 77.0  | 154  | 0.7282          | 0.7794    | 0.8503 | 0.8133 | 0.8839   |
| No log        | 78.0  | 156  | 0.7252          | 0.7783    | 0.8449 | 0.8103 | 0.8839   |
| No log        | 79.0  | 158  | 0.7216          | 0.7756    | 0.8503 | 0.8112 | 0.8855   |
| No log        | 80.0  | 160  | 0.7194          | 0.7756    | 0.8503 | 0.8112 | 0.8870   |
| No log        | 81.0  | 162  | 0.7191          | 0.7756    | 0.8503 | 0.8112 | 0.8878   |
| No log        | 82.0  | 164  | 0.7201          | 0.7696    | 0.8396 | 0.8031 | 0.8862   |
| No log        | 83.0  | 166  | 0.7211          | 0.7696    | 0.8396 | 0.8031 | 0.8862   |
| No log        | 84.0  | 168  | 0.7222          | 0.7696    | 0.8396 | 0.8031 | 0.8862   |
| No log        | 85.0  | 170  | 0.7220          | 0.7696    | 0.8396 | 0.8031 | 0.8862   |
| No log        | 86.0  | 172  | 0.7239          | 0.7734    | 0.8396 | 0.8051 | 0.8870   |
| No log        | 87.0  | 174  | 0.7291          | 0.7772    | 0.8396 | 0.8072 | 0.8847   |
| No log        | 88.0  | 176  | 0.7344          | 0.7745    | 0.8449 | 0.8082 | 0.8824   |
| No log        | 89.0  | 178  | 0.7373          | 0.7745    | 0.8449 | 0.8082 | 0.8824   |
| No log        | 90.0  | 180  | 0.7391          | 0.7707    | 0.8449 | 0.8061 | 0.8832   |
| No log        | 91.0  | 182  | 0.7403          | 0.7745    | 0.8449 | 0.8082 | 0.8824   |
| No log        | 92.0  | 184  | 0.7412          | 0.7745    | 0.8449 | 0.8082 | 0.8832   |
| No log        | 93.0  | 186  | 0.7417          | 0.7707    | 0.8449 | 0.8061 | 0.8832   |
| No log        | 94.0  | 188  | 0.7402          | 0.7745    | 0.8449 | 0.8082 | 0.8839   |
| No log        | 95.0  | 190  | 0.7389          | 0.7745    | 0.8449 | 0.8082 | 0.8847   |
| No log        | 96.0  | 192  | 0.7381          | 0.7696    | 0.8396 | 0.8031 | 0.8839   |
| No log        | 97.0  | 194  | 0.7377          | 0.7696    | 0.8396 | 0.8031 | 0.8847   |
| No log        | 98.0  | 196  | 0.7374          | 0.7696    | 0.8396 | 0.8031 | 0.8847   |
| No log        | 99.0  | 198  | 0.7372          | 0.7696    | 0.8396 | 0.8031 | 0.8847   |
| No log        | 100.0 | 200  | 0.7372          | 0.7696    | 0.8396 | 0.8031 | 0.8847   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "test", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.7696078431372549, "name": "Precision"}, {"type": "recall", "value": 0.839572192513369, "name": "Recall"}, {"type": "f1", "value": 0.8030690537084398, "name": "F1"}, {"type": "accuracy", "value": 0.8847040737893928, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:conll2003 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

test
====


This model is a fine-tuned version of hfl/chinese-bert-wwm-ext on the conll2003 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7372
* Precision: 0.7696
* Recall: 0.8396
* F1: 0.8031
* Accuracy: 0.8847


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1