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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 sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0030
- Precision: 1.0
- Recall: 0.9980
- F1: 0.9990
- Accuracy: 0.9998

## 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  | 0.0715          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| No log        | 4.0   | 200  | 0.0228          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| No log        | 6.0   | 300  | 0.0174          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| No log        | 8.0   | 400  | 0.0137          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| 0.1189        | 10.0  | 500  | 0.0122          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| 0.1189        | 12.0  | 600  | 0.0112          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| 0.1189        | 14.0  | 700  | 0.0080          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| 0.1189        | 16.0  | 800  | 0.0100          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| 0.1189        | 18.0  | 900  | 0.0040          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0097        | 20.0  | 1000 | 0.0030          | 1.0       | 0.9980 | 0.9990 | 0.9998   |
| 0.0097        | 22.0  | 1100 | 0.0028          | 0.9980    | 0.9959 | 0.9970 | 0.9996   |
| 0.0097        | 24.0  | 1200 | 0.0016          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0097        | 26.0  | 1300 | 0.0015          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0097        | 28.0  | 1400 | 0.0015          | 0.9980    | 0.9980 | 0.9980 | 0.9998   |
| 0.0029        | 30.0  | 1500 | 0.0017          | 0.9980    | 0.9980 | 0.9980 | 0.9998   |
| 0.0029        | 32.0  | 1600 | 0.0026          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0029        | 34.0  | 1700 | 0.0026          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0029        | 36.0  | 1800 | 0.0026          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0029        | 38.0  | 1900 | 0.0025          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.002         | 40.0  | 2000 | 0.0026          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |


### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-finetuned-invoice", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "args": "sroie"}, "metrics": [{"type": "precision", "value": 1.0, "name": "Precision"}, {"type": "recall", "value": 0.9979716024340771, "name": "Recall"}, {"type": "f1", "value": 0.9989847715736041, "name": "F1"}, {"type": "accuracy", "value": 0.9997893406361913, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard layoutlmv3 token-classification generated_from_trainer dataset:sroie model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #model-index #autotrain_compatible #endpoints_compatible #region-us 

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


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


* Loss: 0.0030
* Precision: 1.0
* Recall: 0.9980
* F1: 0.9990
* Accuracy: 0.9998


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 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### 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 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #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 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #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 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1