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# distilbert-base-uncased-finetuned-ner

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1660
- Precision: 0.9701
- Recall: 0.9679
- F1: 0.9690
- Accuracy: 0.9863

## 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: 11

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0091        | 1.0   | 5372  | 0.1034          | 0.9693    | 0.9649 | 0.9671 | 0.9858   |
| 0.0052        | 2.0   | 10744 | 0.1362          | 0.9715    | 0.9679 | 0.9697 | 0.9868   |
| 0.0064        | 3.0   | 16116 | 0.1415          | 0.9715    | 0.9657 | 0.9686 | 0.9844   |
| 0.0026        | 4.0   | 21488 | 0.1629          | 0.9709    | 0.9701 | 0.9705 | 0.9870   |
| 0.0034        | 5.0   | 26860 | 0.1345          | 0.9737    | 0.9687 | 0.9712 | 0.9851   |
| 0.0019        | 6.0   | 32232 | 0.1297          | 0.9700    | 0.9649 | 0.9675 | 0.9841   |
| 0.0031        | 7.0   | 37604 | 0.1543          | 0.9716    | 0.9701 | 0.9709 | 0.9868   |
| 0.0021        | 8.0   | 42976 | 0.0605          | 0.9782    | 0.9716 | 0.9749 | 0.9903   |
| 0.0023        | 9.0   | 48348 | 0.1506          | 0.9731    | 0.9701 | 0.9716 | 0.9877   |
| 0.0021        | 10.0  | 53720 | 0.1714          | 0.9693    | 0.9672 | 0.9682 | 0.9860   |
| 0.0015        | 11.0  | 59092 | 0.1660          | 0.9701    | 0.9679 | 0.9690 | 0.9863   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3

{"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 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-finetuned-ner
=====================================


This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1660
* Precision: 0.9701
* Recall: 0.9679
* F1: 0.9690
* Accuracy: 0.9863


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: 11


### Training results



### Framework versions


* Transformers 4.30.2
* Pytorch 1.13.1+cu117
* Datasets 2.13.1
* Tokenizers 0.13.3



### 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: 11 ### Training results ### Framework versions


* Transformers 4.30.2
* Pytorch 1.13.1+cu117
* Datasets 2.13.1
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #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: 11 ### Training results ### Framework versions


* Transformers 4.30.2
* Pytorch 1.13.1+cu117
* Datasets 2.13.1
* Tokenizers 0.13.3