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# rhenus_v3.3

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: 0.1519
- Precision: 0.8149
- Recall: 0.8528
- F1: 0.8334
- Accuracy: 0.9717

## 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: 4000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.43  | 100  | 0.9034          | 0.0270    | 0.0010 | 0.0020 | 0.8485   |
| No log        | 0.85  | 200  | 0.7801          | 0.0270    | 0.0010 | 0.0020 | 0.8485   |
| No log        | 1.28  | 300  | 0.6812          | 0.0635    | 0.0041 | 0.0078 | 0.8490   |
| No log        | 1.7   | 400  | 0.6205          | 0.2037    | 0.0912 | 0.1260 | 0.8599   |
| 0.7202        | 2.13  | 500  | 0.5414          | 0.2296    | 0.1285 | 0.1648 | 0.8682   |
| 0.7202        | 2.55  | 600  | 0.4841          | 0.3139    | 0.2010 | 0.2451 | 0.8769   |
| 0.7202        | 2.98  | 700  | 0.4284          | 0.3782    | 0.2477 | 0.2993 | 0.8931   |
| 0.7202        | 3.4   | 800  | 0.3919          | 0.4783    | 0.3762 | 0.4211 | 0.9028   |
| 0.7202        | 3.83  | 900  | 0.3352          | 0.5749    | 0.4891 | 0.5286 | 0.9223   |
| 0.367         | 4.26  | 1000 | 0.3130          | 0.5913    | 0.5368 | 0.5627 | 0.9300   |
| 0.367         | 4.68  | 1100 | 0.2665          | 0.6218    | 0.5979 | 0.6096 | 0.9358   |
| 0.367         | 5.11  | 1200 | 0.2553          | 0.6336    | 0.6218 | 0.6276 | 0.9398   |
| 0.367         | 5.53  | 1300 | 0.2521          | 0.6391    | 0.6570 | 0.6479 | 0.9376   |
| 0.367         | 5.96  | 1400 | 0.2140          | 0.6719    | 0.6684 | 0.6701 | 0.9474   |
| 0.197         | 6.38  | 1500 | 0.2143          | 0.6889    | 0.7181 | 0.7032 | 0.9497   |
| 0.197         | 6.81  | 1600 | 0.1994          | 0.7202    | 0.7202 | 0.7202 | 0.9528   |
| 0.197         | 7.23  | 1700 | 0.2078          | 0.6657    | 0.7202 | 0.6919 | 0.9473   |
| 0.197         | 7.66  | 1800 | 0.1871          | 0.7357    | 0.7440 | 0.7398 | 0.9562   |
| 0.197         | 8.09  | 1900 | 0.1904          | 0.7024    | 0.7606 | 0.7303 | 0.9533   |
| 0.1183        | 8.51  | 2000 | 0.1770          | 0.7398    | 0.7689 | 0.7541 | 0.9582   |
| 0.1183        | 8.94  | 2100 | 0.1684          | 0.7485    | 0.7710 | 0.7596 | 0.9590   |
| 0.1183        | 9.36  | 2200 | 0.1729          | 0.7412    | 0.7896 | 0.7647 | 0.9590   |
| 0.1183        | 9.79  | 2300 | 0.1610          | 0.765     | 0.7927 | 0.7786 | 0.9612   |
| 0.1183        | 10.21 | 2400 | 0.1570          | 0.7696    | 0.7927 | 0.7810 | 0.9635   |
| 0.0776        | 10.64 | 2500 | 0.1646          | 0.7854    | 0.8041 | 0.7947 | 0.9647   |
| 0.0776        | 11.06 | 2600 | 0.1605          | 0.7702    | 0.8197 | 0.7942 | 0.9659   |
| 0.0776        | 11.49 | 2700 | 0.1575          | 0.7856    | 0.8280 | 0.8063 | 0.9655   |
| 0.0776        | 11.91 | 2800 | 0.1566          | 0.7777    | 0.8228 | 0.7996 | 0.9665   |
| 0.0776        | 12.34 | 2900 | 0.1500          | 0.7840    | 0.8238 | 0.8034 | 0.9673   |
| 0.0586        | 12.77 | 3000 | 0.1577          | 0.7879    | 0.8238 | 0.8055 | 0.9686   |
| 0.0586        | 13.19 | 3100 | 0.1514          | 0.8100    | 0.8259 | 0.8179 | 0.9698   |
| 0.0586        | 13.62 | 3200 | 0.1597          | 0.7864    | 0.8394 | 0.8120 | 0.9679   |
| 0.0586        | 14.04 | 3300 | 0.1492          | 0.8118    | 0.8404 | 0.8259 | 0.9711   |
| 0.0586        | 14.47 | 3400 | 0.1496          | 0.8014    | 0.8404 | 0.8204 | 0.9700   |
| 0.0465        | 14.89 | 3500 | 0.1508          | 0.8061    | 0.8446 | 0.8249 | 0.9703   |
| 0.0465        | 15.32 | 3600 | 0.1539          | 0.8055    | 0.8497 | 0.8270 | 0.9697   |
| 0.0465        | 15.74 | 3700 | 0.1537          | 0.8073    | 0.8508 | 0.8285 | 0.9700   |
| 0.0465        | 16.17 | 3800 | 0.1505          | 0.8187    | 0.8518 | 0.8349 | 0.9716   |
| 0.0465        | 16.6  | 3900 | 0.1518          | 0.8158    | 0.8539 | 0.8344 | 0.9717   |
| 0.0399        | 17.02 | 4000 | 0.1519          | 0.8149    | 0.8528 | 0.8334 | 0.9717   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+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_v3.3", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.8148514851485148, "name": "Precision"}, {"type": "recall", "value": 0.8528497409326425, "name": "Recall"}, {"type": "f1", "value": 0.8334177215189873, "name": "F1"}, {"type": "accuracy", "value": 0.9717280813214739, "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\_v3.3
============


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: 0.1519
* Precision: 0.8149
* Recall: 0.8528
* F1: 0.8334
* Accuracy: 0.9717


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: 4000


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+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: 4000 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+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: 4000 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+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: 4000### Training results### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3