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# rhenus_v3.4

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: 0.1510
- Precision: 0.8351
- Recall: 0.8767
- F1: 0.8554
- Accuracy: 0.9763

## 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: 6000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.43  | 100  | 0.1573          | 0.7621    | 0.8332 | 0.7960 | 0.9633   |
| No log        | 0.85  | 200  | 0.1619          | 0.8087    | 0.8280 | 0.8182 | 0.9682   |
| No log        | 1.28  | 300  | 0.1650          | 0.7720    | 0.8280 | 0.799  | 0.9652   |
| No log        | 1.7   | 400  | 0.1498          | 0.7973    | 0.8518 | 0.8236 | 0.9693   |
| 0.0484        | 2.13  | 500  | 0.1508          | 0.8059    | 0.8518 | 0.8282 | 0.9690   |
| 0.0484        | 2.55  | 600  | 0.1542          | 0.8006    | 0.8487 | 0.8239 | 0.9679   |
| 0.0484        | 2.98  | 700  | 0.1660          | 0.7750    | 0.8528 | 0.8120 | 0.9657   |
| 0.0484        | 3.4   | 800  | 0.1644          | 0.7644    | 0.8642 | 0.8113 | 0.9649   |
| 0.0484        | 3.83  | 900  | 0.1723          | 0.7577    | 0.8394 | 0.7965 | 0.9630   |
| 0.0322        | 4.26  | 1000 | 0.1759          | 0.8153    | 0.8373 | 0.8262 | 0.9692   |
| 0.0322        | 4.68  | 1100 | 0.1610          | 0.7893    | 0.8539 | 0.8203 | 0.9689   |
| 0.0322        | 5.11  | 1200 | 0.1680          | 0.8107    | 0.8301 | 0.8203 | 0.9690   |
| 0.0322        | 5.53  | 1300 | 0.1512          | 0.7955    | 0.8425 | 0.8183 | 0.9692   |
| 0.0322        | 5.96  | 1400 | 0.1699          | 0.7597    | 0.8549 | 0.8045 | 0.9639   |
| 0.024         | 6.38  | 1500 | 0.1574          | 0.8165    | 0.8622 | 0.8387 | 0.9701   |
| 0.024         | 6.81  | 1600 | 0.1466          | 0.8236    | 0.8518 | 0.8375 | 0.9735   |
| 0.024         | 7.23  | 1700 | 0.1487          | 0.8       | 0.8539 | 0.8261 | 0.9709   |
| 0.024         | 7.66  | 1800 | 0.1453          | 0.8213    | 0.8570 | 0.8387 | 0.9709   |
| 0.024         | 8.09  | 1900 | 0.1538          | 0.8186    | 0.8653 | 0.8413 | 0.9720   |
| 0.0177        | 8.51  | 2000 | 0.1562          | 0.8035    | 0.8642 | 0.8328 | 0.9709   |
| 0.0177        | 8.94  | 2100 | 0.1645          | 0.8099    | 0.8518 | 0.8303 | 0.9695   |
| 0.0177        | 9.36  | 2200 | 0.1467          | 0.8148    | 0.8663 | 0.8398 | 0.9719   |
| 0.0177        | 9.79  | 2300 | 0.1504          | 0.8418    | 0.8601 | 0.8508 | 0.9754   |
| 0.0177        | 10.21 | 2400 | 0.1622          | 0.8402    | 0.8394 | 0.8398 | 0.9719   |
| 0.0118        | 10.64 | 2500 | 0.1343          | 0.8465    | 0.8746 | 0.8603 | 0.9757   |
| 0.0118        | 11.06 | 2600 | 0.1540          | 0.8328    | 0.8570 | 0.8447 | 0.9724   |
| 0.0118        | 11.49 | 2700 | 0.1592          | 0.8206    | 0.8487 | 0.8344 | 0.9714   |
| 0.0118        | 11.91 | 2800 | 0.1479          | 0.8365    | 0.8746 | 0.8551 | 0.9743   |
| 0.0118        | 12.34 | 2900 | 0.1682          | 0.8087    | 0.8715 | 0.8389 | 0.9714   |
| 0.0097        | 12.77 | 3000 | 0.1519          | 0.8046    | 0.8663 | 0.8343 | 0.9724   |
| 0.0097        | 13.19 | 3100 | 0.1486          | 0.8427    | 0.8715 | 0.8569 | 0.9744   |
| 0.0097        | 13.62 | 3200 | 0.1535          | 0.8240    | 0.8684 | 0.8456 | 0.9735   |
| 0.0097        | 14.04 | 3300 | 0.1463          | 0.8302    | 0.8715 | 0.8504 | 0.9743   |
| 0.0097        | 14.47 | 3400 | 0.1541          | 0.8162    | 0.8653 | 0.8400 | 0.9714   |
| 0.0074        | 14.89 | 3500 | 0.1668          | 0.8126    | 0.8674 | 0.8391 | 0.9719   |
| 0.0074        | 15.32 | 3600 | 0.1652          | 0.8291    | 0.8549 | 0.8418 | 0.9720   |
| 0.0074        | 15.74 | 3700 | 0.1698          | 0.8234    | 0.8601 | 0.8414 | 0.9720   |
| 0.0074        | 16.17 | 3800 | 0.1620          | 0.8193    | 0.8788 | 0.848  | 0.9735   |
| 0.0074        | 16.6  | 3900 | 0.1608          | 0.8459    | 0.8591 | 0.8524 | 0.9743   |
| 0.0063        | 17.02 | 4000 | 0.1619          | 0.8227    | 0.8653 | 0.8434 | 0.9740   |
| 0.0063        | 17.45 | 4100 | 0.1530          | 0.8229    | 0.8715 | 0.8465 | 0.9743   |
| 0.0063        | 17.87 | 4200 | 0.1563          | 0.8287    | 0.8725 | 0.8501 | 0.9738   |
| 0.0063        | 18.3  | 4300 | 0.1594          | 0.8091    | 0.8694 | 0.8382 | 0.9730   |
| 0.0063        | 18.72 | 4400 | 0.1569          | 0.8251    | 0.8705 | 0.8472 | 0.9746   |
| 0.0049        | 19.15 | 4500 | 0.1473          | 0.8664    | 0.8736 | 0.8700 | 0.9781   |
| 0.0049        | 19.57 | 4600 | 0.1597          | 0.8167    | 0.8725 | 0.8437 | 0.9725   |
| 0.0049        | 20.0  | 4700 | 0.1552          | 0.8223    | 0.8725 | 0.8467 | 0.9740   |
| 0.0049        | 20.43 | 4800 | 0.1614          | 0.8400    | 0.8756 | 0.8574 | 0.9749   |
| 0.0049        | 20.85 | 4900 | 0.1519          | 0.8386    | 0.8725 | 0.8553 | 0.9759   |
| 0.0039        | 21.28 | 5000 | 0.1582          | 0.8273    | 0.8736 | 0.8498 | 0.9738   |
| 0.0039        | 21.7  | 5100 | 0.1618          | 0.8200    | 0.8736 | 0.8460 | 0.9744   |
| 0.0039        | 22.13 | 5200 | 0.1504          | 0.8309    | 0.8756 | 0.8527 | 0.9752   |
| 0.0039        | 22.55 | 5300 | 0.1512          | 0.8358    | 0.8756 | 0.8553 | 0.9755   |
| 0.0039        | 22.98 | 5400 | 0.1525          | 0.8231    | 0.8777 | 0.8495 | 0.9749   |
| 0.0029        | 23.4  | 5500 | 0.1568          | 0.8328    | 0.8777 | 0.8547 | 0.9746   |
| 0.0029        | 23.83 | 5600 | 0.1495          | 0.8408    | 0.8808 | 0.8603 | 0.9767   |
| 0.0029        | 24.26 | 5700 | 0.1510          | 0.8371    | 0.8788 | 0.8574 | 0.9763   |
| 0.0029        | 24.68 | 5800 | 0.1548          | 0.8278    | 0.8767 | 0.8515 | 0.9754   |
| 0.0029        | 25.11 | 5900 | 0.1524          | 0.8319    | 0.8767 | 0.8537 | 0.9759   |
| 0.0027        | 25.53 | 6000 | 0.1510          | 0.8351    | 0.8767 | 0.8554 | 0.9763   |


### 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_v3.4", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.8351431391905232, "name": "Precision"}, {"type": "recall", "value": 0.8766839378238342, "name": "Recall"}, {"type": "f1", "value": 0.8554095045500505, "name": "F1"}, {"type": "accuracy", "value": 0.9763341804320204, "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\_v3.4
============


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: 0.1510
* Precision: 0.8351
* Recall: 0.8767
* F1: 0.8554
* Accuracy: 0.9763


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: 6000


### 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: 6000 ### 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: 6000 ### 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: 6000### Training results### Framework versions


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