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# rhenus_v3.5

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: 0.1811
- Precision: 0.8134
- Recall: 0.8412
- F1: 0.8271
- Accuracy: 0.9642

## 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: 5500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.42  | 100  | 0.8800          | 0.0256    | 0.0010 | 0.0019 | 0.8497   |
| No log        | 0.83  | 200  | 0.7576          | 0.0256    | 0.0010 | 0.0019 | 0.8497   |
| No log        | 1.25  | 300  | 0.6665          | 0.1045    | 0.0141 | 0.0248 | 0.8539   |
| No log        | 1.67  | 400  | 0.5845          | 0.2211    | 0.0673 | 0.1032 | 0.8669   |
| 0.7141        | 2.08  | 500  | 0.5312          | 0.2549    | 0.1166 | 0.16   | 0.8778   |
| 0.7141        | 2.5   | 600  | 0.4970          | 0.3097    | 0.1759 | 0.2244 | 0.8772   |
| 0.7141        | 2.92  | 700  | 0.4455          | 0.3603    | 0.2281 | 0.2794 | 0.8909   |
| 0.7141        | 3.33  | 800  | 0.3984          | 0.4536    | 0.3146 | 0.3715 | 0.9069   |
| 0.7141        | 3.75  | 900  | 0.3447          | 0.4848    | 0.3839 | 0.4285 | 0.9149   |
| 0.361         | 4.17  | 1000 | 0.3283          | 0.5557    | 0.5618 | 0.5587 | 0.9238   |
| 0.361         | 4.58  | 1100 | 0.3005          | 0.6024    | 0.5528 | 0.5765 | 0.9280   |
| 0.361         | 5.0   | 1200 | 0.2841          | 0.6866    | 0.6010 | 0.6409 | 0.9372   |
| 0.361         | 5.42  | 1300 | 0.2407          | 0.6731    | 0.6995 | 0.6861 | 0.9448   |
| 0.361         | 5.83  | 1400 | 0.2486          | 0.7316    | 0.6794 | 0.7045 | 0.9477   |
| 0.1955        | 6.25  | 1500 | 0.2110          | 0.7038    | 0.7045 | 0.7042 | 0.9490   |
| 0.1955        | 6.67  | 1600 | 0.2023          | 0.7075    | 0.7367 | 0.7218 | 0.9504   |
| 0.1955        | 7.08  | 1700 | 0.2137          | 0.6864    | 0.7457 | 0.7148 | 0.9454   |
| 0.1955        | 7.5   | 1800 | 0.2022          | 0.6903    | 0.7548 | 0.7211 | 0.9496   |
| 0.1955        | 7.92  | 1900 | 0.1818          | 0.7160    | 0.7678 | 0.7410 | 0.9562   |
| 0.1152        | 8.33  | 2000 | 0.1980          | 0.7132    | 0.7548 | 0.7334 | 0.9492   |
| 0.1152        | 8.75  | 2100 | 0.1930          | 0.7386    | 0.7638 | 0.7510 | 0.9519   |
| 0.1152        | 9.17  | 2200 | 0.1955          | 0.7333    | 0.7930 | 0.7620 | 0.9539   |
| 0.1152        | 9.58  | 2300 | 0.1815          | 0.7363    | 0.7719 | 0.7537 | 0.9572   |
| 0.1152        | 10.0  | 2400 | 0.1760          | 0.7905    | 0.8    | 0.7952 | 0.9627   |
| 0.0777        | 10.42 | 2500 | 0.1756          | 0.7532    | 0.7638 | 0.7585 | 0.9583   |
| 0.0777        | 10.83 | 2600 | 0.1901          | 0.7779    | 0.7920 | 0.7849 | 0.9595   |
| 0.0777        | 11.25 | 2700 | 0.1918          | 0.7371    | 0.7920 | 0.7636 | 0.9533   |
| 0.0777        | 11.67 | 2800 | 0.1744          | 0.8018    | 0.8090 | 0.8054 | 0.9644   |
| 0.0777        | 12.08 | 2900 | 0.1762          | 0.7805    | 0.8040 | 0.7921 | 0.9612   |
| 0.0562        | 12.5  | 3000 | 0.1774          | 0.7892    | 0.8090 | 0.7990 | 0.9595   |
| 0.0562        | 12.92 | 3100 | 0.1770          | 0.7722    | 0.8141 | 0.7926 | 0.9591   |
| 0.0562        | 13.33 | 3200 | 0.1712          | 0.8249    | 0.8191 | 0.8220 | 0.9661   |
| 0.0562        | 13.75 | 3300 | 0.1773          | 0.7909    | 0.8211 | 0.8057 | 0.9609   |
| 0.0562        | 14.17 | 3400 | 0.1964          | 0.7640    | 0.8201 | 0.7911 | 0.9594   |
| 0.0411        | 14.58 | 3500 | 0.1867          | 0.7797    | 0.8111 | 0.7951 | 0.9618   |
| 0.0411        | 15.0  | 3600 | 0.1850          | 0.7983    | 0.8271 | 0.8124 | 0.9610   |
| 0.0411        | 15.42 | 3700 | 0.1747          | 0.7948    | 0.8251 | 0.8097 | 0.9621   |
| 0.0411        | 15.83 | 3800 | 0.1861          | 0.7813    | 0.8332 | 0.8064 | 0.9588   |
| 0.0411        | 16.25 | 3900 | 0.1805          | 0.7935    | 0.8342 | 0.8133 | 0.9635   |
| 0.0313        | 16.67 | 4000 | 0.1910          | 0.7874    | 0.8261 | 0.8063 | 0.9609   |
| 0.0313        | 17.08 | 4100 | 0.1739          | 0.7856    | 0.8251 | 0.8049 | 0.9620   |
| 0.0313        | 17.5  | 4200 | 0.1828          | 0.7916    | 0.8362 | 0.8133 | 0.9621   |
| 0.0313        | 17.92 | 4300 | 0.1850          | 0.8239    | 0.8322 | 0.8280 | 0.9644   |
| 0.0313        | 18.33 | 4400 | 0.1771          | 0.8252    | 0.8352 | 0.8302 | 0.9644   |
| 0.0254        | 18.75 | 4500 | 0.1974          | 0.7971    | 0.8291 | 0.8128 | 0.9604   |
| 0.0254        | 19.17 | 4600 | 0.1865          | 0.8057    | 0.8291 | 0.8172 | 0.9627   |
| 0.0254        | 19.58 | 4700 | 0.1895          | 0.8197    | 0.8362 | 0.8279 | 0.9638   |
| 0.0254        | 20.0  | 4800 | 0.1781          | 0.8107    | 0.8352 | 0.8228 | 0.9642   |
| 0.0254        | 20.42 | 4900 | 0.1751          | 0.8303    | 0.8362 | 0.8332 | 0.9656   |
| 0.0212        | 20.83 | 5000 | 0.1823          | 0.8209    | 0.8382 | 0.8294 | 0.9650   |
| 0.0212        | 21.25 | 5100 | 0.1727          | 0.8246    | 0.8412 | 0.8328 | 0.9653   |
| 0.0212        | 21.67 | 5200 | 0.1758          | 0.7907    | 0.8392 | 0.8142 | 0.9627   |
| 0.0212        | 22.08 | 5300 | 0.1787          | 0.8104    | 0.8422 | 0.8260 | 0.9642   |
| 0.0212        | 22.5  | 5400 | 0.1816          | 0.8134    | 0.8412 | 0.8271 | 0.9639   |
| 0.0184        | 22.92 | 5500 | 0.1811          | 0.8134    | 0.8412 | 0.8271 | 0.9642   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.2.2
- Tokenizers 0.13.3

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "rhenus_v3.5", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.8134110787172012, "name": "Precision"}, {"type": "recall", "value": 0.8412060301507538, "name": "Recall"}, {"type": "f1", "value": 0.8270750988142292, "name": "F1"}, {"type": "accuracy", "value": 0.9642422398052344, "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\_v3.5
============


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: 0.1811
* Precision: 0.8134
* Recall: 0.8412
* F1: 0.8271
* Accuracy: 0.9642


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: 5500


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3



### 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: 5500 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3
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: 5500 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3