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# layoutlm-funsd-sequence-tf

This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2348
- Validation Loss: 0.6737
- Train Overall Precision: 0.7356
- Train Overall Recall: 0.7998
- Train Overall F1: 0.7663
- Train Overall Accuracy: 0.8220
- Epoch: 7

## 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:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16

### Training results

| Train Loss | Validation Loss | Train Overall Precision | Train Overall Recall | Train Overall F1 | Train Overall Accuracy | Epoch |
|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:|
| 1.7150     | 1.4139          | 0.2373                  | 0.2860               | 0.2594           | 0.4954                 | 0     |
| 1.1803     | 0.9205          | 0.5676                  | 0.6322               | 0.5981           | 0.7008                 | 1     |
| 0.7884     | 0.7100          | 0.6202                  | 0.7250               | 0.6685           | 0.7735                 | 2     |
| 0.5877     | 0.6476          | 0.6689                  | 0.7662               | 0.7142           | 0.7942                 | 3     |
| 0.4490     | 0.6179          | 0.7133                  | 0.8078               | 0.7576           | 0.8066                 | 4     |
| 0.3746     | 0.6305          | 0.7176                  | 0.7878               | 0.7510           | 0.8129                 | 5     |
| 0.3082     | 0.6924          | 0.7163                  | 0.8018               | 0.7566           | 0.7937                 | 6     |
| 0.2348     | 0.6737          | 0.7356                  | 0.7998               | 0.7663           | 0.8220                 | 7     |


### Framework versions

- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3

{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "layoutlm-funsd-sequence-tf", "results": []}]}
transformers tf tensorboard layoutlm token-classification generated_from_keras_callback autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #tensorboard #layoutlm #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 

layoutlm-funsd-sequence-tf
==========================


This model is a fine-tuned version of microsoft/layoutlm-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Train Loss: 0.2348
* Validation Loss: 0.6737
* Train Overall Precision: 0.7356
* Train Overall Recall: 0.7998
* Train Overall F1: 0.7663
* Train Overall Accuracy: 0.8220
* Epoch: 7


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:


* optimizer: {'inner\_optimizer': {'class\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\_rate': 3e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.01}}, 'dynamic': True, 'initial\_scale': 32768.0, 'dynamic\_growth\_steps': 2000}
* training\_precision: mixed\_float16


### Training results



### Framework versions


* Transformers 4.28.1
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'inner\_optimizer': {'class\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\_rate': 3e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.01}}, 'dynamic': True, 'initial\_scale': 32768.0, 'dynamic\_growth\_steps': 2000}
* training\_precision: mixed\_float16 ### Training results ### Framework versions


* Transformers 4.28.1
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #tf #tensorboard #layoutlm #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'inner\_optimizer': {'class\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\_rate': 3e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.01}}, 'dynamic': True, 'initial\_scale': 32768.0, 'dynamic\_growth\_steps': 2000}
* training\_precision: mixed\_float16 ### Training results ### Framework versions


* Transformers 4.28.1
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #tf #tensorboard #layoutlm #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'inner\_optimizer': {'class\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\_rate': 3e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.01}}, 'dynamic': True, 'initial\_scale': 32768.0, 'dynamic\_growth\_steps': 2000}
* training\_precision: mixed\_float16### Training results### Framework versions


* Transformers 4.28.1
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.3