---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-ner-invoiceSenderRecipient-all-inv-26-12
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilbert-base-uncased-ner-invoiceSenderRecipient-all-inv-26-12

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0240
- Precision: 0.8517
- Recall: 0.9006
- F1: 0.8755
- Accuracy: 0.9918

## 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

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0302        | 0.06  | 500   | 0.0312          | 0.8199    | 0.8274 | 0.8236 | 0.9892   |
| 0.028         | 0.11  | 1000  | 0.0308          | 0.8108    | 0.8591 | 0.8343 | 0.9894   |
| 0.0255        | 0.17  | 1500  | 0.0319          | 0.8278    | 0.8226 | 0.8252 | 0.9890   |
| 0.0253        | 0.23  | 2000  | 0.0314          | 0.8046    | 0.8674 | 0.8348 | 0.9893   |
| 0.0253        | 0.28  | 2500  | 0.0329          | 0.7914    | 0.8783 | 0.8326 | 0.9887   |
| 0.0239        | 0.34  | 3000  | 0.0309          | 0.7907    | 0.8871 | 0.8361 | 0.9893   |
| 0.0238        | 0.4   | 3500  | 0.0313          | 0.8109    | 0.8822 | 0.8450 | 0.9898   |
| 0.0242        | 0.46  | 4000  | 0.0292          | 0.8290    | 0.8646 | 0.8464 | 0.9902   |
| 0.0239        | 0.51  | 4500  | 0.0302          | 0.7938    | 0.8859 | 0.8373 | 0.9895   |
| 0.0237        | 0.57  | 5000  | 0.0291          | 0.8246    | 0.8795 | 0.8512 | 0.9903   |
| 0.0254        | 0.63  | 5500  | 0.0296          | 0.8160    | 0.8884 | 0.8507 | 0.9901   |
| 0.0248        | 0.68  | 6000  | 0.0270          | 0.8269    | 0.8845 | 0.8547 | 0.9906   |
| 0.029         | 0.74  | 6500  | 0.0271          | 0.8283    | 0.8874 | 0.8568 | 0.9906   |
| 0.0277        | 0.8   | 7000  | 0.0259          | 0.8374    | 0.8823 | 0.8593 | 0.9909   |
| 0.0276        | 0.85  | 7500  | 0.0264          | 0.8317    | 0.8930 | 0.8612 | 0.9909   |
| 0.0263        | 0.91  | 8000  | 0.0252          | 0.8412    | 0.8901 | 0.8650 | 0.9911   |
| 0.0271        | 0.97  | 8500  | 0.0247          | 0.8531    | 0.8756 | 0.8642 | 0.9913   |
| 0.0242        | 1.02  | 9000  | 0.0256          | 0.8459    | 0.8909 | 0.8678 | 0.9913   |
| 0.022         | 1.08  | 9500  | 0.0262          | 0.8310    | 0.9000 | 0.8641 | 0.9910   |
| 0.0212        | 1.14  | 10000 | 0.0251          | 0.8581    | 0.8780 | 0.8679 | 0.9914   |
| 0.0215        | 1.19  | 10500 | 0.0255          | 0.8441    | 0.8952 | 0.8689 | 0.9914   |
| 0.0209        | 1.25  | 11000 | 0.0253          | 0.8378    | 0.8982 | 0.8669 | 0.9913   |
| 0.021         | 1.31  | 11500 | 0.0253          | 0.8358    | 0.9049 | 0.8690 | 0.9913   |
| 0.0211        | 1.37  | 12000 | 0.0252          | 0.8437    | 0.8989 | 0.8704 | 0.9915   |
| 0.0205        | 1.42  | 12500 | 0.0249          | 0.8464    | 0.8980 | 0.8714 | 0.9916   |
| 0.0206        | 1.48  | 13000 | 0.0247          | 0.8440    | 0.8973 | 0.8698 | 0.9916   |
| 0.02          | 1.54  | 13500 | 0.0246          | 0.8528    | 0.8939 | 0.8729 | 0.9916   |
| 0.0208        | 1.59  | 14000 | 0.0249          | 0.8397    | 0.9063 | 0.8718 | 0.9915   |
| 0.0205        | 1.65  | 14500 | 0.0241          | 0.8549    | 0.8932 | 0.8736 | 0.9917   |
| 0.0204        | 1.71  | 15000 | 0.0241          | 0.8534    | 0.8976 | 0.8749 | 0.9918   |
| 0.0196        | 1.76  | 15500 | 0.0246          | 0.8464    | 0.9038 | 0.8741 | 0.9917   |
| 0.0202        | 1.82  | 16000 | 0.0239          | 0.8514    | 0.8990 | 0.8746 | 0.9918   |
| 0.0197        | 1.88  | 16500 | 0.0242          | 0.8494    | 0.9008 | 0.8744 | 0.9917   |
| 0.0198        | 1.93  | 17000 | 0.0240          | 0.8514    | 0.9005 | 0.8752 | 0.9918   |
| 0.0202        | 1.99  | 17500 | 0.0240          | 0.8517    | 0.9007 | 0.8755 | 0.9918   |


### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0
- Datasets 2.3.2
- Tokenizers 0.10.3