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# layoutlm-funsd

This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7205
- Answer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070}
- Overall Precision: 0.0
- Overall Recall: 0.0
- Overall F1: 0.0
- Overall Accuracy: 0.2854

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                      | Header                                                      | Question                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8104        | 1.0   | 19   | 1.7227          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |
| 1.7658        | 2.0   | 38   | 1.7254          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |
| 1.7511        | 3.0   | 57   | 1.7137          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |
| 1.7532        | 4.0   | 76   | 1.7184          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |
| 1.7589        | 5.0   | 95   | 1.7141          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |
| 1.748         | 6.0   | 114  | 1.7016          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |
| 1.7487        | 7.0   | 133  | 1.7239          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |
| 1.7483        | 8.0   | 152  | 1.7207          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |
| 1.7465        | 9.0   | 171  | 1.7119          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |
| 1.7458        | 10.0  | 190  | 1.7169          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |
| 1.7419        | 11.0  | 209  | 1.7125          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |
| 1.7425        | 12.0  | 228  | 1.7218          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |
| 1.7424        | 13.0  | 247  | 1.7250          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |
| 1.7412        | 14.0  | 266  | 1.7232          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |
| 1.7389        | 15.0  | 285  | 1.7205          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0               | 0.0            | 0.0        | 0.2854           |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "datasets": ["funsd"], "model-index": [{"name": "layoutlm-funsd", "results": []}]}
transformers pytorch tensorboard layoutlm token-classification generated_from_trainer dataset:funsd autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #layoutlm #token-classification #generated_from_trainer #dataset-funsd #autotrain_compatible #endpoints_compatible #region-us 

layoutlm-funsd
==============


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


* Loss: 1.7205
* Answer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}
* Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
* Question: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070}
* Overall Precision: 0.0
* Overall Recall: 0.0
* Overall F1: 0.0
* Overall Accuracy: 0.2854


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: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #layoutlm #token-classification #generated_from_trainer #dataset-funsd #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3