## LayoutLMv3-Fine-Tuning-Invoice Model

#### Model description
**LayoutLMv3-Fine-Tuning-Invoice Model** is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the invoice dataset. For the fine-tuning, We used [Invoice Dataset](https://huggingface.co/datasets/darentang/generated) 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
{"tags": ["generated_from_trainer"], "datasets": ["invoice"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-fine-tuning-invoice", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "Invoice", "type": "invoice", "args": "invoice"}, "metrics": [{"type": "precision", "value": 1.0, "name": "Precision"}, {"type": "recall", "value": 1.0, "name": "Recall"}, {"type": "f1", "value": 1.0, "name": "F1"}, {"type": "accuracy", "value": 1.0, "name": "Accuracy"}]}]}]}
transformers pytorch layoutlmv3 token-classification generated_from_trainer dataset:invoice model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #layoutlmv3 #token-classification #generated_from_trainer #dataset-invoice #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

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



### Framework versions


* Transformers 4.20.1



#### 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 ### Framework versions


* Transformers 4.20.1
TAGS
#transformers #pytorch #layoutlmv3 #token-classification #generated_from_trainer #dataset-invoice #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 #### 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 ### Framework versions


* Transformers 4.20.1
passage: TAGS
#transformers #pytorch #layoutlmv3 #token-classification #generated_from_trainer #dataset-invoice #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
#### 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### Framework versions


* Transformers 4.20.1