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---
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 |