---
library_name: transformers
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: 1.5594
- Answer: {'precision': 0.03052064631956912, 'recall': 0.042027194066749075, 'f1': 0.035361414456578255, 'number': 809}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.18327402135231316, 'recall': 0.19342723004694837, 'f1': 0.18821379625399726, 'number': 1065}
- Overall Precision: 0.1072
- Overall Recall: 0.1204
- Overall F1: 0.1134
- Overall Accuracy: 0.4038

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8318        | 1.0   | 10   | 1.6681          | {'precision': 0.009746588693957114, 'recall': 0.012360939431396786, 'f1': 0.010899182561307902, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0981169474727453, 'recall': 0.09295774647887324, 'f1': 0.09546769527483126, 'number': 1065}  | 0.0535            | 0.0547         | 0.0541     | 0.3444           |
| 1.5836        | 2.0   | 20   | 1.5594          | {'precision': 0.03052064631956912, 'recall': 0.042027194066749075, 'f1': 0.035361414456578255, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.18327402135231316, 'recall': 0.19342723004694837, 'f1': 0.18821379625399726, 'number': 1065} | 0.1072            | 0.1204         | 0.1134     | 0.4038           |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0