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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- format_dataset
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: format_dataset
type: format_dataset
config: assesment dataset
split: test
args: assesment dataset
metrics:
- name: Precision
type: precision
value: 0.8869778869778869
- name: Recall
type: recall
value: 0.9025
- name: F1
type: f1
value: 0.8946716232961586
- name: Accuracy
type: accuracy
value: 0.9977016777752241
---
<!-- 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. -->
# test
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the format_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0089
- Precision: 0.8870
- Recall: 0.9025
- F1: 0.8947
- Accuracy: 0.9977
## 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: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.62 | 100 | 0.0405 | 0.0 | 0.0 | 0.0 | 0.9877 |
| No log | 1.25 | 200 | 0.0170 | 0.7538 | 0.735 | 0.7443 | 0.9949 |
| No log | 1.88 | 300 | 0.0131 | 0.7261 | 0.875 | 0.7937 | 0.9956 |
| No log | 2.5 | 400 | 0.0123 | 0.7692 | 0.85 | 0.8076 | 0.9959 |
| 0.0271 | 3.12 | 500 | 0.0105 | 0.8098 | 0.905 | 0.8548 | 0.9968 |
| 0.0271 | 3.75 | 600 | 0.0106 | 0.8460 | 0.8925 | 0.8686 | 0.9972 |
| 0.0271 | 4.38 | 700 | 0.0086 | 0.8504 | 0.895 | 0.8721 | 0.9973 |
| 0.0271 | 5.0 | 800 | 0.0109 | 0.8871 | 0.845 | 0.8656 | 0.9972 |
| 0.0271 | 5.62 | 900 | 0.0085 | 0.8883 | 0.895 | 0.8917 | 0.9977 |
| 0.0042 | 6.25 | 1000 | 0.0089 | 0.8870 | 0.9025 | 0.8947 | 0.9977 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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