---
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: 1.6862
- Answer: {'precision': 0.03633114949374628, 'recall': 0.0754017305315204, 'f1': 0.04903536977491961, 'number': 809}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.11273957158962795, 'recall': 0.18779342723004694, 'f1': 0.14089468122578372, 'number': 1065}
- Overall Precision: 0.0754
- Overall Recall: 0.1310
- Overall F1: 0.0957
- Overall Accuracy: 0.3643

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                     | Header                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8797        | 1.0   | 10   | 1.6862          | {'precision': 0.03633114949374628, 'recall': 0.0754017305315204, 'f1': 0.04903536977491961, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.11273957158962795, 'recall': 0.18779342723004694, 'f1': 0.14089468122578372, 'number': 1065} | 0.0754            | 0.1310         | 0.0957     | 0.3643           |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3