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# distilbert-base-cased-finetuned-ner_0220_J_ORIDATA

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5445
- Precision: 0.8915
- Recall: 0.9466
- F1: 0.9182
- Accuracy: 0.9486

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 111  | 0.2828          | 0.7395    | 0.8322 | 0.7831 | 0.9293   |
| No log        | 2.0   | 222  | 0.2260          | 0.8029    | 0.8941 | 0.8460 | 0.9413   |
| No log        | 3.0   | 333  | 0.2586          | 0.8131    | 0.9034 | 0.8559 | 0.9425   |
| No log        | 4.0   | 444  | 0.2411          | 0.8541    | 0.9178 | 0.8848 | 0.9441   |
| 0.3027        | 5.0   | 555  | 0.2776          | 0.8817    | 0.9220 | 0.9014 | 0.9466   |
| 0.3027        | 6.0   | 666  | 0.2348          | 0.8647    | 0.9314 | 0.8968 | 0.9445   |
| 0.3027        | 7.0   | 777  | 0.2870          | 0.8762    | 0.9297 | 0.9021 | 0.9471   |
| 0.3027        | 8.0   | 888  | 0.3006          | 0.8650    | 0.9288 | 0.8958 | 0.9428   |
| 0.3027        | 9.0   | 999  | 0.3099          | 0.8751    | 0.9263 | 0.9000 | 0.9440   |
| 0.1147        | 10.0  | 1110 | 0.3325          | 0.8781    | 0.9398 | 0.9079 | 0.9464   |
| 0.1147        | 11.0  | 1221 | 0.3437          | 0.8909    | 0.9415 | 0.9155 | 0.9461   |
| 0.1147        | 12.0  | 1332 | 0.3512          | 0.8935    | 0.9390 | 0.9157 | 0.9468   |
| 0.1147        | 13.0  | 1443 | 0.3664          | 0.8791    | 0.9424 | 0.9096 | 0.9477   |
| 0.0673        | 14.0  | 1554 | 0.4068          | 0.8767    | 0.9398 | 0.9072 | 0.9445   |
| 0.0673        | 15.0  | 1665 | 0.4015          | 0.8808    | 0.9390 | 0.9089 | 0.9481   |
| 0.0673        | 16.0  | 1776 | 0.4220          | 0.8874    | 0.9483 | 0.9168 | 0.9467   |
| 0.0673        | 17.0  | 1887 | 0.4313          | 0.8847    | 0.9432 | 0.9130 | 0.9451   |
| 0.0673        | 18.0  | 1998 | 0.4440          | 0.8762    | 0.9415 | 0.9077 | 0.9408   |
| 0.041         | 19.0  | 2109 | 0.4524          | 0.9034    | 0.9508 | 0.9265 | 0.9484   |
| 0.041         | 20.0  | 2220 | 0.4455          | 0.8978    | 0.9458 | 0.9212 | 0.9470   |
| 0.041         | 21.0  | 2331 | 0.4822          | 0.8861    | 0.9492 | 0.9165 | 0.9426   |
| 0.041         | 22.0  | 2442 | 0.4677          | 0.8855    | 0.9441 | 0.9139 | 0.9446   |
| 0.0253        | 23.0  | 2553 | 0.4945          | 0.8858    | 0.9466 | 0.9152 | 0.9473   |
| 0.0253        | 24.0  | 2664 | 0.4882          | 0.8794    | 0.9458 | 0.9114 | 0.9443   |
| 0.0253        | 25.0  | 2775 | 0.5073          | 0.8953    | 0.9424 | 0.9182 | 0.9448   |
| 0.0253        | 26.0  | 2886 | 0.5012          | 0.8986    | 0.9466 | 0.9220 | 0.9473   |
| 0.0253        | 27.0  | 2997 | 0.4975          | 0.8850    | 0.9458 | 0.9144 | 0.9456   |
| 0.0166        | 28.0  | 3108 | 0.4944          | 0.8879    | 0.9466 | 0.9163 | 0.9474   |
| 0.0166        | 29.0  | 3219 | 0.5137          | 0.8915    | 0.9466 | 0.9182 | 0.9472   |
| 0.0166        | 30.0  | 3330 | 0.4924          | 0.8890    | 0.9432 | 0.9153 | 0.9463   |
| 0.0166        | 31.0  | 3441 | 0.5129          | 0.8884    | 0.9508 | 0.9185 | 0.9466   |
| 0.0114        | 32.0  | 3552 | 0.5184          | 0.8940    | 0.9508 | 0.9216 | 0.9448   |
| 0.0114        | 33.0  | 3663 | 0.5237          | 0.9012    | 0.9508 | 0.9254 | 0.9463   |
| 0.0114        | 34.0  | 3774 | 0.5153          | 0.8937    | 0.9475 | 0.9198 | 0.9474   |
| 0.0114        | 35.0  | 3885 | 0.5182          | 0.8947    | 0.95   | 0.9215 | 0.9482   |
| 0.0114        | 36.0  | 3996 | 0.5311          | 0.8937    | 0.9475 | 0.9198 | 0.9481   |
| 0.0087        | 37.0  | 4107 | 0.5334          | 0.8935    | 0.9525 | 0.9221 | 0.9483   |
| 0.0087        | 38.0  | 4218 | 0.5397          | 0.8900    | 0.9466 | 0.9175 | 0.9475   |
| 0.0087        | 39.0  | 4329 | 0.5331          | 0.8941    | 0.9449 | 0.9188 | 0.9468   |
| 0.0087        | 40.0  | 4440 | 0.5381          | 0.8962    | 0.9441 | 0.9195 | 0.9460   |
| 0.0069        | 41.0  | 4551 | 0.5394          | 0.8937    | 0.9475 | 0.9198 | 0.9479   |
| 0.0069        | 42.0  | 4662 | 0.5516          | 0.8950    | 0.9466 | 0.9201 | 0.9461   |
| 0.0069        | 43.0  | 4773 | 0.5442          | 0.8949    | 0.9449 | 0.9192 | 0.9452   |
| 0.0069        | 44.0  | 4884 | 0.5427          | 0.8927    | 0.9449 | 0.9181 | 0.9482   |
| 0.0069        | 45.0  | 4995 | 0.5515          | 0.8907    | 0.9458 | 0.9174 | 0.9461   |
| 0.0058        | 46.0  | 5106 | 0.5448          | 0.8930    | 0.9475 | 0.9194 | 0.9481   |
| 0.0058        | 47.0  | 5217 | 0.5475          | 0.896     | 0.9492 | 0.9218 | 0.9487   |
| 0.0058        | 48.0  | 5328 | 0.5444          | 0.8907    | 0.9466 | 0.9178 | 0.9484   |
| 0.0058        | 49.0  | 5439 | 0.5452          | 0.8945    | 0.9483 | 0.9206 | 0.9487   |
| 0.005         | 50.0  | 5550 | 0.5445          | 0.8915    | 0.9466 | 0.9182 | 0.9486   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.13.0+cu117
- Datasets 2.8.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-cased-finetuned-ner_0220_J_ORIDATA", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-cased-finetuned-ner\_0220\_J\_ORIDATA
=====================================================


This model is a fine-tuned version of distilbert-base-cased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5445
* Precision: 0.8915
* Recall: 0.9466
* F1: 0.9182
* Accuracy: 0.9486


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: 50


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.13.0+cu117
* Datasets 2.8.0
* 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: 50 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.13.0+cu117
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #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: 50 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.13.0+cu117
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #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: 50### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.13.0+cu117
* Datasets 2.8.0
* Tokenizers 0.12.1