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# SOCIEDADES3.0

This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0009
- Precision: 0.9555
- Recall: 0.9621
- F1: 0.9588
- Accuracy: 0.9998

## 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        | 0.28  | 100  | 0.0024          | 0.8819    | 0.8759 | 0.8789 | 0.9993   |
| No log        | 0.56  | 200  | 0.0017          | 0.9354    | 0.9483 | 0.9418 | 0.9997   |
| No log        | 0.84  | 300  | 0.0017          | 0.9514    | 0.9448 | 0.9481 | 0.9997   |
| No log        | 1.12  | 400  | 0.0010          | 0.9517    | 0.9517 | 0.9517 | 0.9998   |
| 0.0049        | 1.4   | 500  | 0.0010          | 0.9586    | 0.9586 | 0.9586 | 0.9998   |
| 0.0049        | 1.69  | 600  | 0.0014          | 0.9583    | 0.9517 | 0.9550 | 0.9997   |
| 0.0049        | 1.97  | 700  | 0.0015          | 0.9414    | 0.9414 | 0.9414 | 0.9997   |
| 0.0049        | 2.25  | 800  | 0.0024          | 0.9452    | 0.9517 | 0.9485 | 0.9995   |
| 0.0049        | 2.53  | 900  | 0.0016          | 0.9444    | 0.9379 | 0.9412 | 0.9997   |
| 0.0005        | 2.81  | 1000 | 0.0025          | 0.8947    | 0.9379 | 0.9158 | 0.9994   |
| 0.0005        | 3.09  | 1100 | 0.0011          | 0.9392    | 0.9586 | 0.9488 | 0.9996   |
| 0.0005        | 3.37  | 1200 | 0.0008          | 0.9655    | 0.9655 | 0.9655 | 0.9998   |
| 0.0005        | 3.65  | 1300 | 0.0017          | 0.9375    | 0.9310 | 0.9343 | 0.9996   |
| 0.0005        | 3.93  | 1400 | 0.0009          | 0.9615    | 0.9483 | 0.9549 | 0.9998   |
| 0.0004        | 4.21  | 1500 | 0.0008          | 0.9655    | 0.9655 | 0.9655 | 0.9998   |
| 0.0004        | 4.49  | 1600 | 0.0009          | 0.9655    | 0.9655 | 0.9655 | 0.9998   |
| 0.0004        | 4.78  | 1700 | 0.0009          | 0.9655    | 0.9655 | 0.9655 | 0.9998   |
| 0.0004        | 5.06  | 1800 | 0.0009          | 0.9530    | 0.9793 | 0.9660 | 0.9998   |
| 0.0004        | 5.34  | 1900 | 0.0009          | 0.9555    | 0.9621 | 0.9588 | 0.9998   |
| 0.0002        | 5.62  | 2000 | 0.0009          | 0.9555    | 0.9621 | 0.9588 | 0.9998   |


### Framework versions

- Transformers 4.27.0.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": "SOCIEDADES3.0", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9554794520547946, "name": "Precision"}, {"type": "recall", "value": 0.9620689655172414, "name": "Recall"}, {"type": "f1", "value": 0.9587628865979381, "name": "F1"}, {"type": "accuracy", "value": 0.999778141379406, "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 

SOCIEDADES3.0
=============


This model is a fine-tuned version of microsoft/layoutlmv3-large on the sroie dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0009
* Precision: 0.9555
* Recall: 0.9621
* F1: 0.9588
* Accuracy: 0.9998


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 4.27.0.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: 2000 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* Tokenizers 0.13.2
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 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* Tokenizers 0.13.2
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 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* Tokenizers 0.13.2