|
--- |
|
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 |
|
|
