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---
base_model: layoutlmv3
tags:
- generated_from_trainer
datasets:
- not-lain/sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-sroie
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: not-lain/sroie
type: not-lain/sroie
metrics:
- name: Precision
type: precision
value: 0.9435600578871202
- name: Recall
type: recall
value: 0.9421965317919075
- name: F1
type: f1
value: 0.9428778018799712
- name: Accuracy
type: accuracy
value: 0.9952312354080771
---
<!-- 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-sroie
This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the not-lain/sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0319
- Precision: 0.9436
- Recall: 0.9422
- F1: 0.9429
- Accuracy: 0.9952
## 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: 5e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.5873 | 100 | 0.0429 | 0.8634 | 0.8772 | 0.8703 | 0.9896 |
| No log | 3.1746 | 200 | 0.0222 | 0.9079 | 0.9408 | 0.9241 | 0.9947 |
| No log | 4.7619 | 300 | 0.0285 | 0.9232 | 0.9379 | 0.9305 | 0.9939 |
| No log | 6.3492 | 400 | 0.0291 | 0.9393 | 0.9393 | 0.9393 | 0.9947 |
| 0.0408 | 7.9365 | 500 | 0.0295 | 0.9223 | 0.9436 | 0.9329 | 0.9945 |
| 0.0408 | 9.5238 | 600 | 0.0356 | 0.9223 | 0.9436 | 0.9329 | 0.9936 |
| 0.0408 | 11.1111 | 700 | 0.0318 | 0.9397 | 0.9451 | 0.9424 | 0.9952 |
| 0.0408 | 12.6984 | 800 | 0.0311 | 0.9312 | 0.9393 | 0.9353 | 0.9948 |
| 0.0408 | 14.2857 | 900 | 0.0324 | 0.9394 | 0.9408 | 0.9401 | 0.9950 |
| 0.0035 | 15.8730 | 1000 | 0.0319 | 0.9436 | 0.9422 | 0.9429 | 0.9952 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
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