---
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: 1.3476
- Answer: {'precision': 0.17894736842105263, 'recall': 0.3362175525339926, 'f1': 0.2335766423357664, 'number': 809}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.27942998760842624, 'recall': 0.42347417840375584, 'f1': 0.33669279581933553, 'number': 1065}
- Overall Precision: 0.2307
- Overall Recall: 0.3628
- Overall F1: 0.2820
- Overall Accuracy: 0.4351

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                       | Header                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7432        | 1.0   | 10   | 1.5651          | {'precision': 0.03228782287822878, 'recall': 0.04326328800988875, 'f1': 0.036978341257263604, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.18964259664478483, 'recall': 0.24413145539906103, 'f1': 0.2134646962233169, 'number': 1065}  | 0.1202            | 0.1480         | 0.1326     | 0.3666           |
| 1.5478        | 2.0   | 20   | 1.4279          | {'precision': 0.13696715583508037, 'recall': 0.242274412855377, 'f1': 0.17500000000000002, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.25, 'recall': 0.3652582159624413, 'f1': 0.29683326974437235, 'number': 1065}                 | 0.1958            | 0.2935         | 0.2349     | 0.4085           |
| 1.4112        | 3.0   | 30   | 1.3476          | {'precision': 0.17894736842105263, 'recall': 0.3362175525339926, 'f1': 0.2335766423357664, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.27942998760842624, 'recall': 0.42347417840375584, 'f1': 0.33669279581933553, 'number': 1065} | 0.2307            | 0.3628         | 0.2820     | 0.4351           |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2