---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Output_LayoutLMv3_v6
  results: []
datasets:
- Noureddinesa/LayoutLmv3_v1
---

<!-- 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. -->

# Output_LayoutLMv3_v6

This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1308
- Precision: 0.7788
- Recall: 0.8
- F1: 0.7892
- Accuracy: 0.9637

## 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: 3e-07
- 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: 3000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.61  | 100  | 0.5057          | 0.0       | 0.0    | 0.0    | 0.8962   |
| No log        | 3.23  | 200  | 0.3746          | 0.0       | 0.0    | 0.0    | 0.8962   |
| No log        | 4.84  | 300  | 0.2979          | 0.2143    | 0.0273 | 0.0484 | 0.9014   |
| No log        | 6.45  | 400  | 0.2474          | 0.4444    | 0.1455 | 0.2192 | 0.9135   |
| 0.4794        | 8.06  | 500  | 0.2189          | 0.5       | 0.3    | 0.3750 | 0.9291   |
| 0.4794        | 9.68  | 600  | 0.2031          | 0.5301    | 0.4    | 0.4560 | 0.9325   |
| 0.4794        | 11.29 | 700  | 0.1916          | 0.6       | 0.4636 | 0.5231 | 0.9377   |
| 0.4794        | 12.9  | 800  | 0.1788          | 0.6364    | 0.5727 | 0.6029 | 0.9412   |
| 0.4794        | 14.52 | 900  | 0.1728          | 0.6796    | 0.6364 | 0.6573 | 0.9464   |
| 0.184         | 16.13 | 1000 | 0.1644          | 0.7010    | 0.6182 | 0.6570 | 0.9481   |
| 0.184         | 17.74 | 1100 | 0.1593          | 0.75      | 0.7091 | 0.7290 | 0.9567   |
| 0.184         | 19.35 | 1200 | 0.1520          | 0.7714    | 0.7364 | 0.7535 | 0.9602   |
| 0.184         | 20.97 | 1300 | 0.1420          | 0.7778    | 0.7636 | 0.7706 | 0.9619   |
| 0.184         | 22.58 | 1400 | 0.1427          | 0.7925    | 0.7636 | 0.7778 | 0.9637   |
| 0.1278        | 24.19 | 1500 | 0.1361          | 0.7727    | 0.7727 | 0.7727 | 0.9619   |
| 0.1278        | 25.81 | 1600 | 0.1342          | 0.8019    | 0.7727 | 0.7870 | 0.9654   |
| 0.1278        | 27.42 | 1700 | 0.1310          | 0.8056    | 0.7909 | 0.7982 | 0.9671   |
| 0.1278        | 29.03 | 1800 | 0.1290          | 0.7857    | 0.8    | 0.7928 | 0.9654   |
| 0.1278        | 30.65 | 1900 | 0.1268          | 0.7946    | 0.8091 | 0.8018 | 0.9671   |
| 0.0999        | 32.26 | 2000 | 0.1229          | 0.7768    | 0.7909 | 0.7838 | 0.9637   |
| 0.0999        | 33.87 | 2100 | 0.1305          | 0.8056    | 0.7909 | 0.7982 | 0.9654   |
| 0.0999        | 35.48 | 2200 | 0.1349          | 0.8241    | 0.8091 | 0.8165 | 0.9689   |
| 0.0999        | 37.1  | 2300 | 0.1327          | 0.8018    | 0.8091 | 0.8054 | 0.9654   |
| 0.0999        | 38.71 | 2400 | 0.1289          | 0.8018    | 0.8091 | 0.8054 | 0.9654   |
| 0.0833        | 40.32 | 2500 | 0.1274          | 0.8018    | 0.8091 | 0.8054 | 0.9654   |
| 0.0833        | 41.94 | 2600 | 0.1279          | 0.8018    | 0.8091 | 0.8054 | 0.9654   |
| 0.0833        | 43.55 | 2700 | 0.1295          | 0.8018    | 0.8091 | 0.8054 | 0.9654   |
| 0.0833        | 45.16 | 2800 | 0.1306          | 0.7788    | 0.8    | 0.7892 | 0.9637   |
| 0.0833        | 46.77 | 2900 | 0.1312          | 0.7788    | 0.8    | 0.7892 | 0.9637   |
| 0.0749        | 48.39 | 3000 | 0.1308          | 0.7788    | 0.8    | 0.7892 | 0.9637   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.13.3