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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
- Document Layout
- LayoutLMv3
datasets:
- funsd-layoutlmv3
metrics:
- f1
- accuracy
- recall
- precision
model-index:
- name: layoutlmv3-base-fine_tuned-FUNSD_dataset
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: funsd-layoutlmv3
type: funsd-layoutlmv3
config: funsd
split: test
args: funsd
metrics:
- name: Precision
type: precision
value: 0.8978890525282278
- name: Recall
type: recall
value: 0.9085941381023348
- name: F1
type: f1
value: 0.9032098765432099
- name: Accuracy
type: accuracy
value: 0.8461904195887318
language:
- en
---
# layoutlmv3-base-fine_tuned-FUNSD_dataset
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co./microsoft/layoutlmv3-base) on the funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2956
- Precision: 0.8979
- Recall: 0.9086
- F1: 0.9032
- Accuracy: 0.8462
## Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Document%20AI/Document%20Layout/LayoutLMv3%20with%20FUNSD/Fine%20tuning%20%26%20Evaluation%20-%20LayoutLMv3%20with%20FUNSD.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://huggingface.co./datasets/nielsr/funsd-layoutlmv3
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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
- training_steps: 2000
### Training results
| Train Loss | Epoch | Step | Valid. Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2149 | 1.33 | 100 | 0.2402 | 0.7469 | 0.8212 | 0.7823 | 0.7758 |
| 0.1466 | 2.67 | 200 | 0.1869 | 0.8161 | 0.8838 | 0.8486 | 0.8273 |
| 0.1122 | 4.0 | 300 | 0.1902 | 0.8538 | 0.8997 | 0.8761 | 0.8316 |
| 0.0757 | 5.33 | 400 | 0.1857 | 0.8354 | 0.8927 | 0.8631 | 0.8349 |
| 0.0427 | 6.67 | 500 | 0.2091 | 0.8792 | 0.8897 | 0.8844 | 0.8446 |
| 0.0495 | 8.0 | 600 | 0.2235 | 0.8825 | 0.9031 | 0.8927 | 0.8370 |
| 0.0369 | 9.33 | 700 | 0.2532 | 0.8826 | 0.9146 | 0.8983 | 0.8349 |
| 0.0329 | 10.67 | 800 | 0.2576 | 0.8829 | 0.8992 | 0.8910 | 0.8474 |
| 0.0229 | 12.0 | 900 | 0.2579 | 0.8827 | 0.8937 | 0.8882 | 0.8443 |
| 0.0219 | 13.33 | 1000 | 0.2710 | 0.8710 | 0.8987 | 0.8846 | 0.8347 |
| 0.0191 | 14.67 | 1100 | 0.2582 | 0.8889 | 0.9061 | 0.8974 | 0.8454 |
| 0.0179 | 16.0 | 1200 | 0.2646 | 0.8870 | 0.9006 | 0.8938 | 0.8356 |
| 0.0135 | 17.33 | 1300 | 0.2798 | 0.8949 | 0.9180 | 0.9063 | 0.8512 |
| 0.007 | 18.67 | 1400 | 0.2944 | 0.8988 | 0.9091 | 0.9039 | 0.8455 |
| 0.0064 | 20.0 | 1500 | 0.2822 | 0.8938 | 0.9071 | 0.9004 | 0.8452 |
| 0.0089 | 21.33 | 1600 | 0.3003 | 0.8941 | 0.9101 | 0.9020 | 0.8484 |
| 0.0099 | 22.67 | 1700 | 0.3008 | 0.8942 | 0.9071 | 0.9006 | 0.8439 |
| 0.0069 | 24.0 | 1800 | 0.2965 | 0.8942 | 0.9071 | 0.9006 | 0.8386 |
| 0.0048 | 25.33 | 1900 | 0.2973 | 0.9027 | 0.9076 | 0.9051 | 0.8501 |
| 0.0069 | 26.67 | 2000 | 0.2956 | 0.8979 | 0.9086 | 0.9032 | 0.8462 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3 |