---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutmlv3_thursday_oct_7_v5
  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. -->

# layoutmlv3_thursday_oct_7_v5

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: 0.1171
- Precision: 0.8763
- Recall: 0.9075
- F1: 0.8916
- Accuracy: 0.9821

## 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        | 0.86  | 100  | 0.2961          | 0.8166    | 0.8559 | 0.8358 | 0.9372   |
| No log        | 1.72  | 200  | 0.1481          | 0.8037    | 0.8594 | 0.8306 | 0.9674   |
| No log        | 2.59  | 300  | 0.1191          | 0.8082    | 0.8772 | 0.8413 | 0.9753   |
| No log        | 3.45  | 400  | 0.0892          | 0.8969    | 0.9128 | 0.9048 | 0.9844   |
| 0.29          | 4.31  | 500  | 0.1171          | 0.8763    | 0.9075 | 0.8916 | 0.9821   |
| 0.29          | 5.17  | 600  | 0.0994          | 0.8864    | 0.9021 | 0.8942 | 0.9818   |
| 0.29          | 6.03  | 700  | 0.0851          | 0.8901    | 0.8932 | 0.8917 | 0.9844   |
| 0.29          | 6.9   | 800  | 0.0957          | 0.8425    | 0.8950 | 0.8680 | 0.9814   |
| 0.29          | 7.76  | 900  | 0.0951          | 0.8735    | 0.8968 | 0.8850 | 0.9834   |
| 0.1351        | 8.62  | 1000 | 0.1005          | 0.8772    | 0.9021 | 0.8895 | 0.9840   |


### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1