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# EASA10.0-base-2000-paddle

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: 0.0308
- Precision: 0.9762
- Recall: 0.9670
- F1: 0.9716
- Accuracy: 0.9963

## 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        | 0.88  | 100  | 0.1486          | 0.9387    | 0.9387 | 0.9387 | 0.9890   |
| No log        | 1.75  | 200  | 0.0354          | 0.9763    | 0.9717 | 0.9740 | 0.9963   |
| No log        | 2.63  | 300  | 0.0264          | 0.9811    | 0.9811 | 0.9811 | 0.9972   |
| No log        | 3.51  | 400  | 0.0281          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.1349        | 4.39  | 500  | 0.0292          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.1349        | 5.26  | 600  | 0.0289          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.1349        | 6.14  | 700  | 0.0292          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.1349        | 7.02  | 800  | 0.0279          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.1349        | 7.89  | 900  | 0.0279          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.0147        | 8.77  | 1000 | 0.0288          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.0147        | 9.65  | 1100 | 0.0292          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.0147        | 10.53 | 1200 | 0.0305          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.0147        | 11.4  | 1300 | 0.0296          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.0147        | 12.28 | 1400 | 0.0300          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.0084        | 13.16 | 1500 | 0.0309          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.0084        | 14.04 | 1600 | 0.0299          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.0084        | 14.91 | 1700 | 0.0307          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.0084        | 15.79 | 1800 | 0.0304          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.0084        | 16.67 | 1900 | 0.0310          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |
| 0.0069        | 17.54 | 2000 | 0.0308          | 0.9762    | 0.9670 | 0.9716 | 0.9963   |


### Framework versions

- Transformers 4.29.0.dev0
- 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": "EASA10.0-base-2000-paddle", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9761904761904762, "name": "Precision"}, {"type": "recall", "value": 0.9669811320754716, "name": "Recall"}, {"type": "f1", "value": 0.971563981042654, "name": "F1"}, {"type": "accuracy", "value": 0.996316758747698, "name": "Accuracy"}]}]}]}
transformers pytorch layoutlmv3 token-classification generated_from_trainer dataset:sroie license:cc-by-nc-sa-4.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

EASA10.0-base-2000-paddle
=========================


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: 0.0308
* Precision: 0.9762
* Recall: 0.9670
* F1: 0.9716
* Accuracy: 0.9963


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 4.29.0.dev0
* 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: 2000 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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 4.29.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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 4.29.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3