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# distilbert-base-uncased-ner-invoiceSenderRecipient_clean_inv_27_02

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: 0.0160
- Precision: 0.9514
- Recall: 0.9593
- F1: 0.9553
- Accuracy: 0.9953

## 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: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0078        | 0.09  | 500   | 0.0213          | 0.9269    | 0.9514 | 0.9390 | 0.9937   |
| 0.008         | 0.17  | 1000  | 0.0230          | 0.9246    | 0.9516 | 0.9379 | 0.9935   |
| 0.007         | 0.26  | 1500  | 0.0234          | 0.9400    | 0.9478 | 0.9439 | 0.9942   |
| 0.0065        | 0.34  | 2000  | 0.0238          | 0.9280    | 0.9537 | 0.9406 | 0.9936   |
| 0.0071        | 0.43  | 2500  | 0.0221          | 0.9291    | 0.9570 | 0.9428 | 0.9939   |
| 0.007         | 0.52  | 3000  | 0.0210          | 0.9393    | 0.9457 | 0.9425 | 0.9941   |
| 0.0072        | 0.6   | 3500  | 0.0197          | 0.9448    | 0.9490 | 0.9469 | 0.9945   |
| 0.0071        | 0.69  | 4000  | 0.0196          | 0.9400    | 0.9555 | 0.9477 | 0.9945   |
| 0.0109        | 0.77  | 4500  | 0.0178          | 0.9458    | 0.9499 | 0.9478 | 0.9946   |
| 0.01          | 0.86  | 5000  | 0.0191          | 0.9443    | 0.9489 | 0.9466 | 0.9945   |
| 0.0103        | 0.95  | 5500  | 0.0181          | 0.9466    | 0.9530 | 0.9498 | 0.9947   |
| 0.0081        | 1.03  | 6000  | 0.0191          | 0.9448    | 0.9578 | 0.9512 | 0.9948   |
| 0.0102        | 1.12  | 6500  | 0.0171          | 0.9454    | 0.9550 | 0.9502 | 0.9948   |
| 0.01          | 1.21  | 7000  | 0.0178          | 0.9460    | 0.9584 | 0.9521 | 0.9949   |
| 0.0107        | 1.29  | 7500  | 0.0164          | 0.9498    | 0.9552 | 0.9525 | 0.9950   |
| 0.0107        | 1.38  | 8000  | 0.0166          | 0.9461    | 0.9596 | 0.9528 | 0.9950   |
| 0.0095        | 1.46  | 8500  | 0.0170          | 0.9402    | 0.9626 | 0.9513 | 0.9949   |
| 0.0097        | 1.55  | 9000  | 0.0161          | 0.9455    | 0.9595 | 0.9524 | 0.9950   |
| 0.01          | 1.64  | 9500  | 0.0159          | 0.9502    | 0.9583 | 0.9542 | 0.9952   |
| 0.01          | 1.72  | 10000 | 0.0160          | 0.9488    | 0.9598 | 0.9543 | 0.9952   |
| 0.0095        | 1.81  | 10500 | 0.0157          | 0.9502    | 0.9602 | 0.9552 | 0.9953   |
| 0.0087        | 1.89  | 11000 | 0.0160          | 0.9514    | 0.9593 | 0.9553 | 0.9953   |
| 0.0089        | 1.98  | 11500 | 0.0160          | 0.9502    | 0.9608 | 0.9555 | 0.9953   |


### Framework versions

- Transformers 4.15.0
- Pytorch 1.13.1
- Datasets 2.3.2
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-ner-invoiceSenderRecipient_clean_inv_27_02", "results": []}]}
transformers pytorch distilbert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-ner-invoiceSenderRecipient\_clean\_inv\_27\_02
======================================================================


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: 0.0160
* Precision: 0.9514
* Recall: 0.9593
* F1: 0.9553
* Accuracy: 0.9953


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: 2
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.15.0
* Pytorch 1.13.1
* Datasets 2.3.2
* Tokenizers 0.10.3



### 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: 2
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.15.0
* Pytorch 1.13.1
* Datasets 2.3.2
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #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: 2
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.15.0
* Pytorch 1.13.1
* Datasets 2.3.2
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #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: 2
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.15.0
* Pytorch 1.13.1
* Datasets 2.3.2
* Tokenizers 0.10.3