
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# REPROCESO5.0

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: 0.0180
- Precision: 0.9527
- Recall: 0.9951
- F1: 0.9734
- Accuracy: 0.9976

## 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        | 0.55  | 100  | 0.0157          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| No log        | 1.09  | 200  | 0.0208          | 0.9258    | 0.9852 | 0.9545 | 0.9965   |
| No log        | 1.64  | 300  | 0.0184          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| No log        | 2.19  | 400  | 0.0162          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0268        | 2.73  | 500  | 0.0128          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0268        | 3.28  | 600  | 0.0178          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0268        | 3.83  | 700  | 0.0142          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0268        | 4.37  | 800  | 0.0176          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0268        | 4.92  | 900  | 0.0170          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0028        | 5.46  | 1000 | 0.0165          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0028        | 6.01  | 1100 | 0.0166          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0028        | 6.56  | 1200 | 0.0152          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0028        | 7.1   | 1300 | 0.0162          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0028        | 7.65  | 1400 | 0.0196          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0016        | 8.2   | 1500 | 0.0173          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0016        | 8.74  | 1600 | 0.0169          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0016        | 9.29  | 1700 | 0.0162          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0016        | 9.84  | 1800 | 0.0154          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0016        | 10.38 | 1900 | 0.0180          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |
| 0.0011        | 10.93 | 2000 | 0.0180          | 0.9527    | 0.9951 | 0.9734 | 0.9976   |


### Framework versions

- Transformers 4.28.0.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": "REPROCESO5.0", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9527186761229315, "name": "Precision"}, {"type": "recall", "value": 0.9950617283950617, "name": "Recall"}, {"type": "f1", "value": 0.9734299516908212, "name": "F1"}, {"type": "accuracy", "value": 0.9975708502024292, "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 

REPROCESO5.0
============


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: 0.0180
* Precision: 0.9527
* Recall: 0.9951
* F1: 0.9734
* Accuracy: 0.9976


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 4.28.0.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 4.28.0.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 4.28.0.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 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3