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# layoutlmv3-finetuned-invoice

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the generated dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0070
- Precision: 0.9960
- Recall: 0.9980
- F1: 0.9970
- Accuracy: 0.9996

## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 2.0   | 100  | 0.0947          | 0.892     | 0.9047 | 0.8983 | 0.9886   |
| No log        | 4.0   | 200  | 0.0232          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| No log        | 6.0   | 300  | 0.0175          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| No log        | 8.0   | 400  | 0.0130          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| 0.1292        | 10.0  | 500  | 0.0070          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.1292        | 12.0  | 600  | 0.0081          | 0.9837    | 0.9817 | 0.9827 | 0.9975   |
| 0.1292        | 14.0  | 700  | 0.0045          | 0.9980    | 0.9959 | 0.9970 | 0.9996   |
| 0.1292        | 16.0  | 800  | 0.0026          | 0.9980    | 0.9980 | 0.9980 | 0.9998   |
| 0.1292        | 18.0  | 900  | 0.0024          | 0.9980    | 0.9980 | 0.9980 | 0.9998   |
| 0.0066        | 20.0  | 1000 | 0.0030          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0066        | 22.0  | 1100 | 0.0025          | 0.9980    | 0.9980 | 0.9980 | 0.9998   |
| 0.0066        | 24.0  | 1200 | 0.0019          | 0.9980    | 0.9980 | 0.9980 | 0.9998   |
| 0.0066        | 26.0  | 1300 | 0.0019          | 0.9980    | 0.9980 | 0.9980 | 0.9998   |
| 0.0066        | 28.0  | 1400 | 0.0028          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0026        | 30.0  | 1500 | 0.0022          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0026        | 32.0  | 1600 | 0.0023          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0026        | 34.0  | 1700 | 0.0021          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0026        | 36.0  | 1800 | 0.0033          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0026        | 38.0  | 1900 | 0.0032          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0019        | 40.0  | 2000 | 0.0032          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["generated"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-finetuned-invoice", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "generated", "type": "generated", "config": "sroie", "split": "test", "args": "sroie"}, "metrics": [{"type": "precision", "value": 0.9959514170040485, "name": "Precision"}, {"type": "recall", "value": 0.9979716024340771, "name": "Recall"}, {"type": "f1", "value": 0.9969604863221885, "name": "F1"}, {"type": "accuracy", "value": 0.9995786812723826, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard layoutlmv3 token-classification generated_from_trainer dataset:generated license:cc-by-nc-sa-4.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-generated #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

layoutlmv3-finetuned-invoice
============================


This model is a fine-tuned version of microsoft/layoutlmv3-base on the generated dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0070
* Precision: 0.9960
* Recall: 0.9980
* F1: 0.9970
* Accuracy: 0.9996


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: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2000


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2000 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-generated #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2000 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-generated #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2000### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2