---
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
model-index:
- name: layoutlm-funsd
  results: []
---

<!-- 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. -->

# layoutlm-funsd

This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0668
- Number-a: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
- Number-q: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
- Overall Precision: 0.0
- Overall Recall: 0.0
- Overall F1: 0.0
- Overall Accuracy: 0.9848

## 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: 16
- eval_batch_size: 8
- 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 | Number-a                                                  | Number-q                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.1627        | 1.0   | 1    | 1.1422          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.2713           |
| 1.1655        | 2.0   | 2    | 1.1422          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.2713           |
| 1.1695        | 3.0   | 3    | 1.1422          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.2713           |
| 1.1661        | 4.0   | 4    | 0.8227          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.8093           |
| 0.8478        | 5.0   | 5    | 0.5718          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.9744           |
| 0.5975        | 6.0   | 6    | 0.3821          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.9848           |
| 0.4052        | 7.0   | 7    | 0.2537          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.9848           |
| 0.2676        | 8.0   | 8    | 0.1673          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.9848           |
| 0.1775        | 9.0   | 9    | 0.1173          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.9848           |
| 0.1266        | 10.0  | 10   | 0.0942          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.9848           |
| 0.1017        | 11.0  | 11   | 0.0842          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.9848           |
| 0.0891        | 12.0  | 12   | 0.0786          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.9848           |
| 0.0845        | 13.0  | 13   | 0.0741          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.9848           |
| 0.0788        | 14.0  | 14   | 0.0702          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.9848           |
| 0.0763        | 15.0  | 15   | 0.0668          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0               | 0.0            | 0.0        | 0.9848           |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0