---
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: 1.6482
- Answer: {'precision': 0.014705882352941176, 'recall': 0.021013597033374538, 'f1': 0.017302798982188297, 'number': 809}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.12369791666666667, 'recall': 0.0892018779342723, 'f1': 0.10365521003818877, 'number': 1065}
- Overall Precision: 0.0582
- Overall Recall: 0.0562
- Overall F1: 0.0572
- Overall Accuracy: 0.3618

## 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: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                      | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8182        | 1.0   | 10   | 1.6482          | {'precision': 0.014705882352941176, 'recall': 0.021013597033374538, 'f1': 0.017302798982188297, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.12369791666666667, 'recall': 0.0892018779342723, 'f1': 0.10365521003818877, 'number': 1065} | 0.0582            | 0.0562         | 0.0572     | 0.3618           |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.15.2