---
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
model-index:
- name: lilt-invoices
  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. -->

# lilt-invoices

This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0031
- Endorname: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 177}
- Escription: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 183}
- Illingaddress: {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161}
- Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 175}
- Nitprice: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 156}
- Nvoicedate: {'precision': 0.9941520467836257, 'recall': 1.0, 'f1': 0.9970674486803519, 'number': 170}
- Nvoicetotal: {'precision': 0.9946808510638298, 'recall': 0.9946808510638298, 'f1': 0.9946808510638298, 'number': 188}
- Otaltax: {'precision': 1.0, 'recall': 0.9927007299270073, 'f1': 0.9963369963369962, 'number': 137}
- Uantity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 167}
- Ubtotal: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 151}
- Overall Precision: 0.9988
- Overall Recall: 0.9982
- Overall F1: 0.9985
- Overall Accuracy: 0.9994

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Endorname                                                   | Escription                                                  | Illingaddress                                                                             | Mount                                                       | Nitprice                                                    | Nvoicedate                                                                                | Nvoicetotal                                                                                              | Otaltax                                                                                   | Uantity                                                     | Ubtotal                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.1736        | 21.74 | 500  | 0.0031          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 177} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 183} | {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 175} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 156} | {'precision': 0.9941520467836257, 'recall': 1.0, 'f1': 0.9970674486803519, 'number': 170} | {'precision': 0.9946808510638298, 'recall': 0.9946808510638298, 'f1': 0.9946808510638298, 'number': 188} | {'precision': 1.0, 'recall': 0.9927007299270073, 'f1': 0.9963369963369962, 'number': 137} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 167} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 151} | 0.9988            | 0.9982         | 0.9985     | 0.9994           |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3