---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layout-xlm-base-finetuned-DocLayNet-base_lines_ml384-v1
  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. -->

# layout-xlm-base-finetuned-DocLayNet-base_lines_ml384-v1

This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2364
- Precision: 0.7260
- Recall: 0.7415
- F1: 0.7336
- Accuracy: 0.9373

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Accuracy | F1     | Validation Loss | Precision | Recall |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|:---------:|:------:|
| No log        | 0.12  | 300  | 0.8413   | 0.1311 | 0.5185          | 0.1437    | 0.1205 |
| 0.9231        | 0.25  | 600  | 0.8751   | 0.5031 | 0.4108          | 0.4637    | 0.5498 |
| 0.9231        | 0.37  | 900  | 0.8887   | 0.5206 | 0.3911          | 0.5076    | 0.5343 |
| 0.369         | 0.5   | 1200 | 0.8724   | 0.5365 | 0.4118          | 0.5094    | 0.5667 |
| 0.2737        | 0.62  | 1500 | 0.8960   | 0.6033 | 0.3328          | 0.6046    | 0.6020 |
| 0.2737        | 0.75  | 1800 | 0.9186   | 0.6404 | 0.2984          | 0.6062    | 0.6787 |
| 0.2542        | 0.87  | 2100 | 0.9163   | 0.6593 | 0.3115          | 0.6324    | 0.6887 |
| 0.2542        | 1.0   | 2400 | 0.9198   | 0.6537 | 0.2878          | 0.6160    | 0.6962 |
| 0.1938        | 1.12  | 2700 | 0.9165   | 0.6752 | 0.3414          | 0.6673    | 0.6833 |
| 0.1581        | 1.25  | 3000 | 0.9193   | 0.6871 | 0.3611          | 0.6868    | 0.6875 |
| 0.1581        | 1.37  | 3300 | 0.9256   | 0.6822 | 0.2763          | 0.6988    | 0.6663 |
| 0.1428        | 1.5   | 3600 | 0.9287   | 0.7084 | 0.3065          | 0.7246    | 0.6929 |
| 0.1428        | 1.62  | 3900 | 0.9194   | 0.6812 | 0.2942          | 0.6866    | 0.6760 |
| 0.1025        | 1.74  | 4200 | 0.9347   | 0.7223 | 0.2990          | 0.7315    | 0.7133 |
| 0.1225        | 1.87  | 4500 | 0.9360   | 0.7048 | 0.2729          | 0.7249    | 0.6858 |
| 0.1225        | 1.99  | 4800 | 0.9396   | 0.7222 | 0.2826          | 0.7497    | 0.6966 |
| 0.108         | 2.12  | 5100 | 0.9301   | 0.7193 | 0.3071          | 0.7022    | 0.7372 |
| 0.108         | 2.24  | 5400 | 0.9334   | 0.7243 | 0.2999          | 0.7250    | 0.7237 |
| 0.0799        | 2.37  | 5700 | 0.9382   | 0.7254 | 0.2710          | 0.7310    | 0.7198 |
| 0.0793        | 2.49  | 6000 | 0.9329   | 0.7228 | 0.3201          | 0.7352    | 0.7108 |
| 0.0793        | 2.62  | 6300 | 0.9373   | 0.7336 | 0.3035          | 0.7260    | 0.7415 |
| 0.0696        | 2.74  | 6600 | 0.9374   | 0.7275 | 0.3137          | 0.7313    | 0.7237 |
| 0.0696        | 2.87  | 6900 | 0.9381   | 0.7253 | 0.3242          | 0.7369    | 0.7142 |
| 0.0866        | 2.99  | 7200 | 0.2473   | 0.7439 | 0.7207          | 0.7321    | 0.9407 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.10.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2