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# distilbert-base-cased-finetuned-ner_0301_J_DATA

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: 0.0356
- Precision: 0.9588
- Recall: 0.9664
- F1: 0.9626
- Accuracy: 0.9933

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2272        | 1.0   | 705   | 0.0711          | 0.8795    | 0.9249 | 0.9016 | 0.9777   |
| 0.0551        | 2.0   | 1410  | 0.0439          | 0.9222    | 0.9563 | 0.9389 | 0.9878   |
| 0.0253        | 3.0   | 2115  | 0.0397          | 0.9312    | 0.9563 | 0.9436 | 0.9898   |
| 0.0277        | 4.0   | 2820  | 0.0500          | 0.9492    | 0.9641 | 0.9566 | 0.9898   |
| 0.018         | 5.0   | 3525  | 0.0414          | 0.9524    | 0.9652 | 0.9588 | 0.9902   |
| 0.0154        | 6.0   | 4230  | 0.0383          | 0.9397    | 0.9608 | 0.9501 | 0.9892   |
| 0.0133        | 7.0   | 4935  | 0.0454          | 0.9408    | 0.9619 | 0.9512 | 0.9888   |
| 0.0073        | 8.0   | 5640  | 0.0343          | 0.9496    | 0.9709 | 0.9601 | 0.9917   |
| 0.0071        | 9.0   | 6345  | 0.0295          | 0.9524    | 0.9652 | 0.9588 | 0.9923   |
| 0.0053        | 10.0  | 7050  | 0.0307          | 0.9449    | 0.9619 | 0.9533 | 0.9929   |
| 0.0048        | 11.0  | 7755  | 0.0221          | 0.9304    | 0.9585 | 0.9442 | 0.9935   |
| 0.0022        | 12.0  | 8460  | 0.0338          | 0.9450    | 0.9630 | 0.9539 | 0.9923   |
| 0.0024        | 13.0  | 9165  | 0.0263          | 0.9578    | 0.9675 | 0.9626 | 0.9938   |
| 0.0017        | 14.0  | 9870  | 0.0345          | 0.9547    | 0.9697 | 0.9622 | 0.9935   |
| 0.0016        | 15.0  | 10575 | 0.0356          | 0.9588    | 0.9664 | 0.9626 | 0.9933   |


### 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_0301_J_DATA", "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\_0301\_J\_DATA
==================================================


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: 0.0356
* Precision: 0.9588
* Recall: 0.9664
* F1: 0.9626
* Accuracy: 0.9933


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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### 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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### 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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### 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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15### Training results### Framework versions


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