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# rhenus_v2.0_cleared

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: 0.2695
- Precision: 0.9566
- Recall: 0.9566
- F1: 0.9566
- Accuracy: 0.9556

## 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: 3000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.91  | 100  | 2.3292          | 0.2072    | 0.1074 | 0.1415 | 0.3949   |
| No log        | 1.82  | 200  | 1.5407          | 0.6009    | 0.5475 | 0.5730 | 0.6939   |
| No log        | 2.73  | 300  | 0.9994          | 0.7174    | 0.6715 | 0.6937 | 0.8084   |
| No log        | 3.64  | 400  | 0.7563          | 0.7692    | 0.7645 | 0.7668 | 0.8435   |
| 1.6153        | 4.55  | 500  | 0.5786          | 0.8071    | 0.8037 | 0.8054 | 0.8692   |
| 1.6153        | 5.45  | 600  | 0.5156          | 0.7988    | 0.7955 | 0.7971 | 0.8680   |
| 1.6153        | 6.36  | 700  | 0.4419          | 0.8693    | 0.8657 | 0.8675 | 0.8984   |
| 1.6153        | 7.27  | 800  | 0.3940          | 0.8786    | 0.8822 | 0.8804 | 0.9077   |
| 1.6153        | 8.18  | 900  | 0.3359          | 0.9156    | 0.9194 | 0.9175 | 0.9252   |
| 0.4054        | 9.09  | 1000 | 0.3066          | 0.9262    | 0.9339 | 0.9300 | 0.9381   |
| 0.4054        | 10.0  | 1100 | 0.2570          | 0.9270    | 0.9442 | 0.9355 | 0.9568   |
| 0.4054        | 10.91 | 1200 | 0.2439          | 0.9406    | 0.9483 | 0.9444 | 0.9603   |
| 0.4054        | 11.82 | 1300 | 0.2378          | 0.9446    | 0.9504 | 0.9475 | 0.9614   |
| 0.4054        | 12.73 | 1400 | 0.2502          | 0.9388    | 0.9504 | 0.9446 | 0.9521   |
| 0.1461        | 13.64 | 1500 | 0.2008          | 0.9569    | 0.9628 | 0.9598 | 0.9696   |
| 0.1461        | 14.55 | 1600 | 0.2454          | 0.9446    | 0.9504 | 0.9475 | 0.9544   |
| 0.1461        | 15.45 | 1700 | 0.2234          | 0.9609    | 0.9649 | 0.9629 | 0.9673   |
| 0.1461        | 16.36 | 1800 | 0.2408          | 0.9526    | 0.9545 | 0.9536 | 0.9544   |
| 0.1461        | 17.27 | 1900 | 0.2620          | 0.9545    | 0.9545 | 0.9545 | 0.9544   |
| 0.0693        | 18.18 | 2000 | 0.2170          | 0.9588    | 0.9628 | 0.9608 | 0.9673   |
| 0.0693        | 19.09 | 2100 | 0.2408          | 0.9588    | 0.9607 | 0.9598 | 0.9603   |
| 0.0693        | 20.0  | 2200 | 0.2450          | 0.9506    | 0.9545 | 0.9526 | 0.9556   |
| 0.0693        | 20.91 | 2300 | 0.2437          | 0.9545    | 0.9545 | 0.9545 | 0.9544   |
| 0.0693        | 21.82 | 2400 | 0.2173          | 0.9548    | 0.9607 | 0.9578 | 0.9685   |
| 0.0472        | 22.73 | 2500 | 0.2428          | 0.9485    | 0.9504 | 0.9494 | 0.9626   |
| 0.0472        | 23.64 | 2600 | 0.2659          | 0.9525    | 0.9525 | 0.9525 | 0.9533   |
| 0.0472        | 24.55 | 2700 | 0.2713          | 0.9566    | 0.9566 | 0.9566 | 0.9556   |
| 0.0472        | 25.45 | 2800 | 0.2803          | 0.9545    | 0.9545 | 0.9545 | 0.9544   |
| 0.0472        | 26.36 | 2900 | 0.2779          | 0.9566    | 0.9566 | 0.9566 | 0.9556   |
| 0.0356        | 27.27 | 3000 | 0.2695          | 0.9566    | 0.9566 | 0.9566 | 0.9556   |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.2.2
- Tokenizers 0.13.2

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "rhenus_v2.0_cleared", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9566115702479339, "name": "Precision"}, {"type": "recall", "value": 0.9566115702479339, "name": "Recall"}, {"type": "f1", "value": 0.9566115702479339, "name": "F1"}, {"type": "accuracy", "value": 0.955607476635514, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard layoutlmv3 token-classification generated_from_trainer dataset:sroie 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-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

rhenus\_v2.0\_cleared
=====================


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: 0.2695
* Precision: 0.9566
* Recall: 0.9566
* F1: 0.9566
* Accuracy: 0.9556


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: 3000


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* 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: 3000 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #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: 3000 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #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: 3000### Training results### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* Tokenizers 0.13.2