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# distilbert-base-uncased-finetuned-ner

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: 0.1080
- Precision: 0.9631
- Recall: 0.9740
- F1: 0.9685
- Accuracy: 0.9774

## 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: 14

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1714        | 1.0   | 1492  | 0.1178          | 0.9209    | 0.9470 | 0.9337 | 0.9613   |
| 0.1018        | 2.0   | 2984  | 0.1062          | 0.9391    | 0.9546 | 0.9468 | 0.9668   |
| 0.0826        | 3.0   | 4476  | 0.0878          | 0.9460    | 0.9577 | 0.9518 | 0.9705   |
| 0.0621        | 4.0   | 5968  | 0.0945          | 0.9507    | 0.9663 | 0.9584 | 0.9728   |
| 0.0567        | 5.0   | 7460  | 0.0891          | 0.9562    | 0.9690 | 0.9625 | 0.9752   |
| 0.0422        | 6.0   | 8952  | 0.0805          | 0.9572    | 0.9699 | 0.9635 | 0.9776   |
| 0.0337        | 7.0   | 10444 | 0.0940          | 0.9575    | 0.9685 | 0.9629 | 0.9752   |
| 0.0282        | 8.0   | 11936 | 0.0940          | 0.9584    | 0.9717 | 0.9650 | 0.9756   |
| 0.0239        | 9.0   | 13428 | 0.0939          | 0.9579    | 0.9717 | 0.9647 | 0.9769   |
| 0.0213        | 10.0  | 14920 | 0.0980          | 0.9618    | 0.9745 | 0.9681 | 0.9775   |
| 0.0192        | 11.0  | 16412 | 0.1075          | 0.9606    | 0.9731 | 0.9668 | 0.9764   |
| 0.0156        | 12.0  | 17904 | 0.1046          | 0.9623    | 0.9734 | 0.9678 | 0.9774   |
| 0.0134        | 13.0  | 19396 | 0.1055          | 0.9614    | 0.9735 | 0.9674 | 0.9769   |
| 0.0125        | 14.0  | 20888 | 0.1080          | 0.9631    | 0.9740 | 0.9685 | 0.9774   |


### 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", "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
=====================================


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: 0.1080
* Precision: 0.9631
* Recall: 0.9740
* F1: 0.9685
* Accuracy: 0.9774


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: 14


### 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: 14 ### 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: 14 ### 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: 14### Training results### Framework versions


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