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---
tags:
- generated_from_trainer
datasets:
- sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-sroie
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: sroie
type: sroie
args: sroie
metrics:
- name: Precision
type: precision
value: 0.9362154500354358
- name: Recall
type: recall
value: 0.9517291066282421
- name: F1
type: f1
value: 0.9439085387638442
- name: Accuracy
type: accuracy
value: 0.9951776838044365
---
<!-- 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 [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0288
- Precision: 0.9362
- Recall: 0.9517
- F1: 0.9439
- 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: 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: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.32 | 100 | 0.1063 | 0.6851 | 0.6599 | 0.6723 | 0.9739 |
| No log | 0.64 | 200 | 0.0583 | 0.7849 | 0.7860 | 0.7855 | 0.9843 |
| No log | 0.96 | 300 | 0.0475 | 0.8463 | 0.8610 | 0.8536 | 0.9884 |
| No log | 1.28 | 400 | 0.0437 | 0.8566 | 0.8739 | 0.8652 | 0.9894 |
| 0.1215 | 1.6 | 500 | 0.0424 | 0.8616 | 0.9063 | 0.8834 | 0.9895 |
| 0.1215 | 1.92 | 600 | 0.0332 | 0.8702 | 0.9323 | 0.9002 | 0.9924 |
| 0.1215 | 2.24 | 700 | 0.0318 | 0.8979 | 0.9373 | 0.9172 | 0.9932 |
| 0.1215 | 2.56 | 800 | 0.0316 | 0.9092 | 0.9445 | 0.9265 | 0.9936 |
| 0.1215 | 2.88 | 900 | 0.0295 | 0.8982 | 0.9467 | 0.9218 | 0.9937 |
| 0.0286 | 3.19 | 1000 | 0.0329 | 0.8685 | 0.9517 | 0.9082 | 0.9930 |
| 0.0286 | 3.51 | 1100 | 0.0289 | 0.9298 | 0.9352 | 0.9325 | 0.9945 |
| 0.0286 | 3.83 | 1200 | 0.0287 | 0.9202 | 0.9474 | 0.9336 | 0.9946 |
| 0.0286 | 4.15 | 1300 | 0.0301 | 0.9174 | 0.9524 | 0.9346 | 0.9947 |
| 0.0286 | 4.47 | 1400 | 0.0268 | 0.9212 | 0.9431 | 0.9320 | 0.9946 |
| 0.017 | 4.79 | 1500 | 0.0307 | 0.9236 | 0.9488 | 0.9360 | 0.9944 |
| 0.017 | 5.11 | 1600 | 0.0286 | 0.9335 | 0.9503 | 0.9418 | 0.9951 |
| 0.017 | 5.43 | 1700 | 0.0287 | 0.9284 | 0.9618 | 0.9448 | 0.9951 |
| 0.017 | 5.75 | 1800 | 0.0278 | 0.9334 | 0.9496 | 0.9414 | 0.9952 |
| 0.017 | 6.07 | 1900 | 0.0289 | 0.9337 | 0.9539 | 0.9437 | 0.9952 |
| 0.0111 | 6.39 | 2000 | 0.0288 | 0.9362 | 0.9517 | 0.9439 | 0.9952 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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