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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: reciept-model-2500
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: 0.9673366834170855
- name: Recall
type: recall
value: 0.9625
- name: F1
type: f1
value: 0.9649122807017544
- name: Accuracy
type: accuracy
value: 0.9993105033325672
---
<!-- 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. -->
# reciept-model-2500
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: 0.0043
- Precision: 0.9673
- Recall: 0.9625
- F1: 0.9649
- Accuracy: 0.9993
## 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: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.62 | 100 | 0.0150 | 0.8575 | 0.8725 | 0.8649 | 0.9972 |
| No log | 1.25 | 200 | 0.0075 | 0.8756 | 0.9325 | 0.9031 | 0.9979 |
| No log | 1.88 | 300 | 0.0154 | 0.8744 | 0.8875 | 0.8809 | 0.9973 |
| No log | 2.5 | 400 | 0.0118 | 0.8881 | 0.9525 | 0.9192 | 0.9982 |
| 0.0029 | 3.12 | 500 | 0.0091 | 0.9158 | 0.925 | 0.9204 | 0.9983 |
| 0.0029 | 3.75 | 600 | 0.0167 | 0.8720 | 0.9025 | 0.8870 | 0.9975 |
| 0.0029 | 4.38 | 700 | 0.0092 | 0.9183 | 0.9275 | 0.9229 | 0.9983 |
| 0.0029 | 5.0 | 800 | 0.0113 | 0.8843 | 0.9175 | 0.9006 | 0.9979 |
| 0.0029 | 5.62 | 900 | 0.0106 | 0.9349 | 0.8975 | 0.9158 | 0.9982 |
| 0.0017 | 6.25 | 1000 | 0.0043 | 0.9673 | 0.9625 | 0.9649 | 0.9993 |
| 0.0017 | 6.88 | 1100 | 0.0044 | 0.9602 | 0.965 | 0.9626 | 0.9993 |
| 0.0017 | 7.5 | 1200 | 0.0118 | 0.9246 | 0.92 | 0.9223 | 0.9982 |
| 0.0017 | 8.12 | 1300 | 0.0067 | 0.9406 | 0.95 | 0.9453 | 0.9988 |
| 0.0017 | 8.75 | 1400 | 0.0083 | 0.9409 | 0.955 | 0.9479 | 0.9989 |
| 0.001 | 9.38 | 1500 | 0.0060 | 0.9495 | 0.94 | 0.9447 | 0.9988 |
| 0.001 | 10.0 | 1600 | 0.0078 | 0.9369 | 0.9275 | 0.9322 | 0.9985 |
| 0.001 | 10.62 | 1700 | 0.0093 | 0.9248 | 0.9525 | 0.9384 | 0.9986 |
| 0.001 | 11.25 | 1800 | 0.0097 | 0.9062 | 0.9425 | 0.9240 | 0.9983 |
| 0.001 | 11.88 | 1900 | 0.0100 | 0.9098 | 0.9325 | 0.9210 | 0.9982 |
| 0.0006 | 12.5 | 2000 | 0.0111 | 0.9113 | 0.925 | 0.9181 | 0.9981 |
| 0.0006 | 13.12 | 2100 | 0.0107 | 0.9275 | 0.9275 | 0.9275 | 0.9983 |
| 0.0006 | 13.75 | 2200 | 0.0105 | 0.9279 | 0.9325 | 0.9302 | 0.9984 |
| 0.0006 | 14.38 | 2300 | 0.0109 | 0.9325 | 0.9325 | 0.9325 | 0.9985 |
| 0.0006 | 15.0 | 2400 | 0.0109 | 0.9325 | 0.9325 | 0.9325 | 0.9985 |
| 0.0003 | 15.62 | 2500 | 0.0109 | 0.9325 | 0.9325 | 0.9325 | 0.9985 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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