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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- funsd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-funsd
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: funsd
type: funsd
config: funsd
split: test
args: funsd
metrics:
- name: Precision
type: precision
value: 0.7998102466793169
- name: Recall
type: recall
value: 0.8375558867362146
- name: F1
type: f1
value: 0.8182479980587235
- name: Accuracy
type: accuracy
value: 0.826102460477832
---
<!-- 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. -->
# layoutlmv3-finetuned-funsd
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0068
- Precision: 0.7998
- Recall: 0.8376
- F1: 0.8182
- Accuracy: 0.8261
## 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: 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: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 3.33 | 250 | 0.5828 | 0.7015 | 0.8033 | 0.7490 | 0.8022 |
| 0.6702 | 6.67 | 500 | 0.5765 | 0.7499 | 0.8073 | 0.7775 | 0.8253 |
| 0.6702 | 10.0 | 750 | 0.7082 | 0.7755 | 0.8236 | 0.7988 | 0.8160 |
| 0.1797 | 13.33 | 1000 | 0.7819 | 0.7807 | 0.8366 | 0.8077 | 0.8256 |
| 0.1797 | 16.67 | 1250 | 0.8199 | 0.7997 | 0.8311 | 0.8151 | 0.8227 |
| 0.0745 | 20.0 | 1500 | 0.9025 | 0.7943 | 0.8286 | 0.8111 | 0.8231 |
| 0.0745 | 23.33 | 1750 | 0.9159 | 0.7941 | 0.8470 | 0.8197 | 0.8248 |
| 0.041 | 26.67 | 2000 | 1.0012 | 0.7989 | 0.8385 | 0.8182 | 0.8210 |
| 0.041 | 30.0 | 2250 | 0.9852 | 0.8024 | 0.8450 | 0.8231 | 0.8301 |
| 0.0246 | 33.33 | 2500 | 1.0068 | 0.7998 | 0.8376 | 0.8182 | 0.8261 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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