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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bank_statement_extractor-v2
results: []
---
<!-- 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. -->
# bank_statement_extractor-v2
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0708
- Precision: 0.9963
- Recall: 0.9963
- F1: 0.9963
- Accuracy: 0.9968
## 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: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 5.0 | 50 | 1.6411 | 0.7203 | 0.6886 | 0.7041 | 0.7302 |
| No log | 10.0 | 100 | 0.7567 | 0.9599 | 0.9634 | 0.9616 | 0.9619 |
| No log | 15.0 | 150 | 0.3852 | 0.9927 | 0.9927 | 0.9927 | 0.9937 |
| No log | 20.0 | 200 | 0.2156 | 0.9927 | 0.9927 | 0.9927 | 0.9937 |
| No log | 25.0 | 250 | 0.1415 | 0.9963 | 0.9963 | 0.9963 | 0.9968 |
| No log | 30.0 | 300 | 0.1087 | 0.9963 | 0.9963 | 0.9963 | 0.9968 |
| No log | 35.0 | 350 | 0.0896 | 0.9963 | 0.9963 | 0.9963 | 0.9968 |
| No log | 40.0 | 400 | 0.0779 | 0.9963 | 0.9963 | 0.9963 | 0.9968 |
| No log | 45.0 | 450 | 0.0720 | 0.9963 | 0.9963 | 0.9963 | 0.9968 |
| 0.4783 | 50.0 | 500 | 0.0708 | 0.9963 | 0.9963 | 0.9963 | 0.9968 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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