---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
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 funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7180
- Answer: {'precision': 0.596, 'recall': 0.7367119901112484, 'f1': 0.6589275843007186, 'number': 809}
- Header: {'precision': 0.08571428571428572, 'recall': 0.05042016806722689, 'f1': 0.06349206349206349, 'number': 119}
- Question: {'precision': 0.6859706362153344, 'recall': 0.7896713615023474, 'f1': 0.7341772151898733, 'number': 1065}
- Overall Precision: 0.6285
- Overall Recall: 0.7240
- Overall F1: 0.6729
- Overall Accuracy: 0.7704

## 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                     | Header                                                                                                       | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.6774        | 1.0   | 19   | 1.3758          | {'precision': 0.06839378238341969, 'recall': 0.0815822002472188, 'f1': 0.07440811724915444, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.332824427480916, 'recall': 0.40938967136150234, 'f1': 0.367157894736842, 'number': 1065}  | 0.2207            | 0.2519         | 0.2352     | 0.4928           |
| 1.169         | 2.0   | 38   | 0.9500          | {'precision': 0.4467425025853154, 'recall': 0.5339925834363412, 'f1': 0.48648648648648646, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.557753164556962, 'recall': 0.6619718309859155, 'f1': 0.605410047230571, 'number': 1065}   | 0.5076            | 0.5705         | 0.5372     | 0.6799           |
| 0.8429        | 3.0   | 57   | 0.7922          | {'precision': 0.5751953125, 'recall': 0.7280593325092707, 'f1': 0.6426623022367702, 'number': 809}         | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.6494676494676495, 'recall': 0.7446009389671362, 'f1': 0.6937882764654418, 'number': 1065} | 0.6051            | 0.6934         | 0.6462     | 0.7457           |
| 0.6915        | 4.0   | 76   | 0.7294          | {'precision': 0.5885262116716122, 'recall': 0.7354758961681088, 'f1': 0.6538461538461539, 'number': 809}   | {'precision': 0.05172413793103448, 'recall': 0.025210084033613446, 'f1': 0.03389830508474576, 'number': 119} | {'precision': 0.6642628205128205, 'recall': 0.7784037558685446, 'f1': 0.7168179853004755, 'number': 1065} | 0.6159            | 0.7160         | 0.6622     | 0.7651           |
| 0.6221        | 5.0   | 95   | 0.7180          | {'precision': 0.596, 'recall': 0.7367119901112484, 'f1': 0.6589275843007186, 'number': 809}                | {'precision': 0.08571428571428572, 'recall': 0.05042016806722689, 'f1': 0.06349206349206349, 'number': 119}  | {'precision': 0.6859706362153344, 'recall': 0.7896713615023474, 'f1': 0.7341772151898733, 'number': 1065} | 0.6285            | 0.7240         | 0.6729     | 0.7704           |


### Framework versions

- Transformers 4.43.4
- Pytorch 2.4.0+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1