README.md

---
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: 0.1475
- Endorname: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
- Escription: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
- Illingaddress: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
- Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
- Nitprice: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}
- Nvoicedate: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
- Nvoicetotal: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
- Otaltax: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
- Uantity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
- Ubtotal: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
- Overall Precision: 1.0
- Overall Recall: 1.0
- Overall F1: 1.0
- Overall Accuracy: 1.0

## 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: 20

### Training results



### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3