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---
library_name: transformers
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: 0.9515865227347072
- name: Recall
type: recall
value: 0.9645225464190982
- name: F1
type: f1
value: 0.9580108677753993
- name: Accuracy
type: accuracy
value: 0.9870755974971792
---
<!-- 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: 0.0596
- Precision: 0.9516
- Recall: 0.9645
- F1: 0.9580
- Accuracy: 0.9871
## 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 | 0.0548 | 0.9415 | 0.9453 | 0.9434 | 0.9830 |
| No log | 3.1746 | 200 | 0.0531 | 0.9377 | 0.9625 | 0.9499 | 0.9847 |
| No log | 4.7619 | 300 | 0.0550 | 0.9414 | 0.9595 | 0.9504 | 0.9851 |
| No log | 6.3492 | 400 | 0.0560 | 0.9500 | 0.9645 | 0.9572 | 0.9868 |
| 0.0464 | 7.9365 | 500 | 0.0596 | 0.9516 | 0.9645 | 0.9580 | 0.9871 |
| 0.0464 | 9.5238 | 600 | 0.0630 | 0.9502 | 0.9622 | 0.9562 | 0.9865 |
| 0.0464 | 11.1111 | 700 | 0.0707 | 0.9489 | 0.9658 | 0.9573 | 0.9868 |
| 0.0464 | 12.6984 | 800 | 0.0726 | 0.9515 | 0.9629 | 0.9572 | 0.9868 |
| 0.0464 | 14.2857 | 900 | 0.0765 | 0.9510 | 0.9652 | 0.9580 | 0.9871 |
| 0.0048 | 15.8730 | 1000 | 0.0773 | 0.9500 | 0.9645 | 0.9572 | 0.9868 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
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