transformers pytorch layoutlm token-classification generated_from_trainer dataset:funsd base_model:microsoft/layoutlm-base-uncased autotrain_compatible endpoints_compatible region:us
---
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm_model
  results: []
---

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# layoutlm_model

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: 1.6891
- Answer: {'precision': 0.020451339915373765, 'recall': 0.03584672435105068, 'f1': 0.026044005388414906, 'number': 809}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.12158341187558906, 'recall': 0.12112676056338029, 'f1': 0.1213546566321731, 'number': 1065}
- Overall Precision: 0.0637
- Overall Recall: 0.0793
- Overall F1: 0.0707
- Overall Accuracy: 0.3724

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8693        | 1.0   | 10   | 1.6891          | {'precision': 0.020451339915373765, 'recall': 0.03584672435105068, 'f1': 0.026044005388414906, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.12158341187558906, 'recall': 0.12112676056338029, 'f1': 0.1213546566321731, 'number': 1065} | 0.0637            | 0.0793         | 0.0707     | 0.3724           |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
