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---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- doc_lay_net-small
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-DocLayNet
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: doc_lay_net-small
type: doc_lay_net-small
config: DocLayNet_2022.08_processed_on_2023.01
split: test
args: DocLayNet_2022.08_processed_on_2023.01
metrics:
- name: Precision
type: precision
value: 0.876231416801003
- name: Recall
type: recall
value: 0.876231416801003
- name: F1
type: f1
value: 0.876231416801003
- name: Accuracy
type: accuracy
value: 0.876231416801003
---
<!-- 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-DocLayNet
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the doc_lay_net-small dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4878
- Precision: 0.8762
- Recall: 0.8762
- F1: 0.8762
- Accuracy: 0.8762
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.1244 | 2.9070 | 250 | 0.7630 | 0.7337 | 0.7337 | 0.7337 | 0.7337 |
| 0.2934 | 5.8140 | 500 | 0.4878 | 0.8762 | 0.8762 | 0.8762 | 0.8762 |
| 0.1028 | 8.7209 | 750 | 0.5626 | 0.8752 | 0.8752 | 0.8752 | 0.8752 |
| 0.0539 | 11.6279 | 1000 | 0.6090 | 0.8719 | 0.8719 | 0.8719 | 0.8719 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
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