metadata
tags:
- generated_from_trainer
datasets:
- invoice
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-fine-tuning-invoice
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: Invoice
type: invoice
args: invoice
metrics:
- name: Precision
type: precision
value: 1
- name: Recall
type: recall
value: 1
- name: F1
type: f1
value: 1
- name: Accuracy
type: accuracy
value: 1
LayoutLMv3-Fine-Tuning-Invoice Model
Model description
LayoutLMv3-Fine-Tuning-Invoice Model is a fine-tuned version of microsoft/layoutlmv3-base on the invoice dataset. For the fine-tuning, We used Invoice Dataset that includes 12 labels ('Other', 'ABN', 'BILLER', 'BILLER_ADDRESS', 'BILLER_POST_CODE', 'DUE_DATE', 'GST', 'INVOICE_DATE', 'INVOICE_NUMBER', 'SUBTOTAL', 'TOTAL', 'BILLER_ADDRESS').
It achieves the following results on the evaluation set:
- Loss: 0.005334
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.5e-05
- train_batch_size: 2
- eval_batch_size: 2
- optimizer: epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
Training results
| Training Loss | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|
| No log | 100 | 0.070030 | 0.972000 | 0.985801 | 0.978852 | 0.997051 |
| No log | 200 | 0.017637 | 0.972000 | 0.985801 | 0.978852 | 0.997051 |
| No log | 300 | 0.015573 | 0.972000 | 0.985801 | 0.978852 | 0.997051 |
| No log | 400 | 0.011000 | 0.973737 | 0.977688 | 0.978852 | 0.996419 |
| 0.110800 | 500 | 0.005334 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.110800 | 600 | 0.002994 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.110800 | 700 | 0.002330 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.110800 | 800 | 0.002188 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.110800 | 900 | 0.002105 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.004900 | 1000 | 0.002111 | 1.0 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.20.1