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# distilbert-base-uncased-finetuned-ner_0212

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1755
- Precision: 0.8894
- Recall: 0.9402
- F1: 0.9141
- Accuracy: 0.9561

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 105  | 0.4362          | 0.6638    | 0.7654 | 0.7110 | 0.8662   |
| No log        | 2.0   | 210  | 0.2527          | 0.7860    | 0.8686 | 0.8252 | 0.9127   |
| No log        | 3.0   | 315  | 0.2411          | 0.8339    | 0.8942 | 0.8630 | 0.9223   |
| No log        | 4.0   | 420  | 0.1801          | 0.8444    | 0.9021 | 0.8723 | 0.9403   |
| 0.3472        | 5.0   | 525  | 0.1569          | 0.8470    | 0.9166 | 0.8804 | 0.9439   |
| 0.3472        | 6.0   | 630  | 0.1424          | 0.8643    | 0.9205 | 0.8915 | 0.9531   |
| 0.3472        | 7.0   | 735  | 0.1526          | 0.8764    | 0.9271 | 0.9010 | 0.9476   |
| 0.3472        | 8.0   | 840  | 0.1804          | 0.8826    | 0.9284 | 0.9049 | 0.9452   |
| 0.3472        | 9.0   | 945  | 0.1650          | 0.8809    | 0.9330 | 0.9062 | 0.9504   |
| 0.068         | 10.0  | 1050 | 0.1458          | 0.8819    | 0.9271 | 0.9039 | 0.9574   |
| 0.068         | 11.0  | 1155 | 0.1618          | 0.8810    | 0.9336 | 0.9065 | 0.9511   |
| 0.068         | 12.0  | 1260 | 0.1817          | 0.8865    | 0.9343 | 0.9098 | 0.9489   |
| 0.068         | 13.0  | 1365 | 0.1530          | 0.8890    | 0.9363 | 0.912  | 0.9572   |
| 0.068         | 14.0  | 1470 | 0.1643          | 0.9032    | 0.9382 | 0.9204 | 0.9552   |
| 0.0342        | 15.0  | 1575 | 0.1710          | 0.9016    | 0.9336 | 0.9174 | 0.9550   |
| 0.0342        | 16.0  | 1680 | 0.1736          | 0.8879    | 0.9369 | 0.9118 | 0.9554   |
| 0.0342        | 17.0  | 1785 | 0.1722          | 0.8903    | 0.9382 | 0.9136 | 0.9556   |
| 0.0342        | 18.0  | 1890 | 0.1713          | 0.8848    | 0.9389 | 0.9111 | 0.9525   |
| 0.0342        | 19.0  | 1995 | 0.1692          | 0.88      | 0.9396 | 0.9088 | 0.9573   |
| 0.0224        | 20.0  | 2100 | 0.1755          | 0.8894    | 0.9402 | 0.9141 | 0.9561   |


### 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-uncased-finetuned-ner_0212", "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-uncased-finetuned-ner\_0212
===========================================


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


* Loss: 0.1755
* Precision: 0.8894
* Recall: 0.9402
* F1: 0.9141
* Accuracy: 0.9561


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: 20


### 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: 20 ### 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: 20 ### 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: 20### Training results### Framework versions


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