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# layoutxlm-finetuned-xfund-es

-> This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) on the xfun dataset.

-> Este modelo es una versión de [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) con ajuste fino sobre xfun-es dataset (español).

## 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 2500

### Training results

Train output:
- global_step=2500
- training_loss=0.3006648193359375
- train_runtime: 3385.7522
- train_samples_per_second: 1.477
- train_steps_per_second: 0.738
- total_flos: 2688500014387200.0
- train_loss: 0.3006648193359375
- epoch: 20.49

### Framework versions

- Transformers 4.26.0
- Pytorch 1.10.0+cu111
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["xfun"], "model-index": [{"name": "layoutxlm-finetuned-xfund-es", "results": []}]}
transformers pytorch tensorboard layoutlmv2 token-classification generated_from_trainer dataset:xfun license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #dataset-xfun #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 




# layoutxlm-finetuned-xfund-es

-> This model is a fine-tuned version of microsoft/layoutxlm-base on the xfun dataset.

-> Este modelo es una versión de microsoft/layoutxlm-base con ajuste fino sobre xfun-es dataset (español).

## 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 2500

### Training results

Train output:
- global_step=2500
- training_loss=0.3006648193359375
- train_runtime: 3385.7522
- train_samples_per_second: 1.477
- train_steps_per_second: 0.738
- total_flos: 2688500014387200.0
- train_loss: 0.3006648193359375
- epoch: 20.49

### Framework versions

- Transformers 4.26.0
- Pytorch 1.10.0+cu111
- Datasets 2.9.0
- Tokenizers 0.13.2

# layoutxlm-finetuned-xfund-es

-> This model is a fine-tuned version of microsoft/layoutxlm-base on the xfun dataset.

-> Este modelo es una versión de microsoft/layoutxlm-base con ajuste fino sobre xfun-es dataset (español). ## 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 2500 ### Training results

Train output:
- global_step=2500
- training_loss=0.3006648193359375
- train_runtime: 3385.7522
- train_samples_per_second: 1.477
- train_steps_per_second: 0.738
- total_flos: 2688500014387200.0
- train_loss: 0.3006648193359375
- epoch: 20.49 ### Framework versions

- Transformers 4.26.0
- Pytorch 1.10.0+cu111
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #dataset-xfun #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # layoutxlm-finetuned-xfund-es

-> This model is a fine-tuned version of microsoft/layoutxlm-base on the xfun dataset.

-> Este modelo es una versión de microsoft/layoutxlm-base con ajuste fino sobre xfun-es dataset (español). ## 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 2500 ### Training results

Train output:
- global_step=2500
- training_loss=0.3006648193359375
- train_runtime: 3385.7522
- train_samples_per_second: 1.477
- train_steps_per_second: 0.738
- total_flos: 2688500014387200.0
- train_loss: 0.3006648193359375
- epoch: 20.49 ### Framework versions

- Transformers 4.26.0
- Pytorch 1.10.0+cu111
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #dataset-xfun #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
# layoutxlm-finetuned-xfund-es

-> This model is a fine-tuned version of microsoft/layoutxlm-base on the xfun dataset.

-> Este modelo es una versión de microsoft/layoutxlm-base con ajuste fino sobre xfun-es dataset (español).## 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 2500### Training results

Train output:
- global_step=2500
- training_loss=0.3006648193359375
- train_runtime: 3385.7522
- train_samples_per_second: 1.477
- train_steps_per_second: 0.738
- total_flos: 2688500014387200.0
- train_loss: 0.3006648193359375
- epoch: 20.49### Framework versions

- Transformers 4.26.0
- Pytorch 1.10.0+cu111
- Datasets 2.9.0
- Tokenizers 0.13.2