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---
tags:
- generated_from_trainer
datasets:
- mp-02/funsd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-funsd
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: mp-02/funsd
type: mp-02/funsd
metrics:
- name: Precision
type: precision
value: 0.875725338491296
- name: Recall
type: recall
value: 0.9055
- name: F1
type: f1
value: 0.8903638151425762
- name: Accuracy
type: accuracy
value: 0.843706936150666
---
<!-- 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 [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6187
- Precision: 0.8757
- Recall: 0.9055
- F1: 0.8904
- Accuracy: 0.8437
## 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: 4
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.32 | 50 | 0.9063 | 0.7006 | 0.757 | 0.7277 | 0.7607 |
| No log | 2.63 | 100 | 0.6387 | 0.7930 | 0.858 | 0.8242 | 0.7967 |
| No log | 3.95 | 150 | 0.5691 | 0.8171 | 0.8825 | 0.8486 | 0.8254 |
| No log | 5.26 | 200 | 0.5723 | 0.8315 | 0.881 | 0.8555 | 0.8223 |
| No log | 6.58 | 250 | 0.5897 | 0.8475 | 0.9 | 0.8729 | 0.8292 |
| No log | 7.89 | 300 | 0.6122 | 0.8482 | 0.9025 | 0.8745 | 0.8283 |
| No log | 9.21 | 350 | 0.6045 | 0.8505 | 0.899 | 0.8741 | 0.8392 |
| No log | 10.53 | 400 | 0.5662 | 0.8708 | 0.9 | 0.8852 | 0.8446 |
| No log | 11.84 | 450 | 0.5973 | 0.8739 | 0.9045 | 0.8889 | 0.8437 |
| 0.4305 | 13.16 | 500 | 0.6187 | 0.8757 | 0.9055 | 0.8904 | 0.8437 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 2.13.2
- Tokenizers 0.10.1
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