---
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: 1.0643
- Answer: {'precision': 0.384928716904277, 'recall': 0.4672435105067985, 'f1': 0.4221105527638191, 'number': 809}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.5277088502894954, 'recall': 0.5990610328638498, 'f1': 0.5611257695690414, 'number': 1065}
- Overall Precision: 0.4583
- Overall Recall: 0.5098
- Overall F1: 0.4827
- Overall Accuracy: 0.6395

## 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.4286        | 1.0   | 75   | 1.0643          | {'precision': 0.384928716904277, 'recall': 0.4672435105067985, 'f1': 0.4221105527638191, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5277088502894954, 'recall': 0.5990610328638498, 'f1': 0.5611257695690414, 'number': 1065} | 0.4583            | 0.5098         | 0.4827     | 0.6395           |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1