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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- violations
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-violations-test
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: violations
type: violations
config: ViolationsExtraction
split: test
args: ViolationsExtraction
metrics:
- name: Precision
type: precision
value: 0.9482758620689655
- name: Recall
type: recall
value: 0.9116022099447514
- name: F1
type: f1
value: 0.9295774647887324
- name: Accuracy
type: accuracy
value: 0.9502762430939227
---
<!-- 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-violations-test
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the violations dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3685
- Precision: 0.9483
- Recall: 0.9116
- F1: 0.9296
- Accuracy: 0.9503
## 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: 8
- eval_batch_size: 8
- 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 | 9.0909 | 100 | 0.2997 | 0.9543 | 0.9227 | 0.9382 | 0.9558 |
| No log | 18.1818 | 200 | 0.3729 | 0.9425 | 0.9061 | 0.9239 | 0.9448 |
| No log | 27.2727 | 300 | 0.3408 | 0.9543 | 0.9227 | 0.9382 | 0.9558 |
| No log | 36.3636 | 400 | 0.3566 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
| 0.0997 | 45.4545 | 500 | 0.3685 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
| 0.0997 | 54.5455 | 600 | 0.3736 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
| 0.0997 | 63.6364 | 700 | 0.3866 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
| 0.0997 | 72.7273 | 800 | 0.3990 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
| 0.0997 | 81.8182 | 900 | 0.4018 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
| 0.001 | 90.9091 | 1000 | 0.3979 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
### Framework versions
- Transformers 4.42.1
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
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