---
language:
- en
- de
- fr
- ja
license: mit
tags:
- generated_from_trainer
- DocLayNet
- COCO
- PDF
- IBM
- Financial-Reports
- Finance
- Manuals
- Scientific-Articles
- Science
- Laws
- Law
- Regulations
- Patents
- Government-Tenders
- object-detection
- image-segmentation
- token-classification
datasets:
- pierreguillou/DocLayNet-base
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-linelevel-ml384
  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. -->

# Document Understanding model (at line level)

This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) with the [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0003
- Precision: 0.8584
- Recall: 0.8584
- F1: 0.8584
- Accuracy: 0.8584

## 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: 5e-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
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7223        | 0.21  | 500   | 0.7765          | 0.7741    | 0.7741 | 0.7741 | 0.7741   |
| 0.4469        | 0.42  | 1000  | 0.5914          | 0.8312    | 0.8312 | 0.8312 | 0.8312   |
| 0.3819        | 0.62  | 1500  | 0.8745          | 0.8102    | 0.8102 | 0.8102 | 0.8102   |
| 0.3361        | 0.83  | 2000  | 0.6991          | 0.8337    | 0.8337 | 0.8337 | 0.8337   |
| 0.2784        | 1.04  | 2500  | 0.7513          | 0.8119    | 0.8119 | 0.8119 | 0.8119   |
| 0.2377        | 1.25  | 3000  | 0.9048          | 0.8166    | 0.8166 | 0.8166 | 0.8166   |
| 0.2401        | 1.45  | 3500  | 1.2411          | 0.7939    | 0.7939 | 0.7939 | 0.7939   |
| 0.2054        | 1.66  | 4000  | 1.1594          | 0.8080    | 0.8080 | 0.8080 | 0.8080   |
| 0.1909        | 1.87  | 4500  | 0.7545          | 0.8425    | 0.8425 | 0.8425 | 0.8425   |
| 0.1704        | 2.08  | 5000  | 0.8567          | 0.8318    | 0.8318 | 0.8318 | 0.8318   |
| 0.1294        | 2.29  | 5500  | 0.8486          | 0.8489    | 0.8489 | 0.8489 | 0.8489   |
| 0.134         | 2.49  | 6000  | 0.7682          | 0.8573    | 0.8573 | 0.8573 | 0.8573   |
| 0.1354        | 2.7   | 6500  | 0.9871          | 0.8256    | 0.8256 | 0.8256 | 0.8256   |
| 0.1239        | 2.91  | 7000  | 1.1430          | 0.8189    | 0.8189 | 0.8189 | 0.8189   |
| 0.1012        | 3.12  | 7500  | 0.8272          | 0.8386    | 0.8386 | 0.8386 | 0.8386   |
| 0.0788        | 3.32  | 8000  | 1.0288          | 0.8365    | 0.8365 | 0.8365 | 0.8365   |
| 0.0802        | 3.53  | 8500  | 0.7197          | 0.8849    | 0.8849 | 0.8849 | 0.8849   |
| 0.0861        | 3.74  | 9000  | 1.1420          | 0.8320    | 0.8320 | 0.8320 | 0.8320   |
| 0.0639        | 3.95  | 9500  | 0.9563          | 0.8585    | 0.8585 | 0.8585 | 0.8585   |
| 0.0464        | 4.15  | 10000 | 1.0768          | 0.8511    | 0.8511 | 0.8511 | 0.8511   |
| 0.0412        | 4.36  | 10500 | 1.1184          | 0.8439    | 0.8439 | 0.8439 | 0.8439   |
| 0.039         | 4.57  | 11000 | 0.9634          | 0.8636    | 0.8636 | 0.8636 | 0.8636   |
| 0.0469        | 4.78  | 11500 | 0.9585          | 0.8634    | 0.8634 | 0.8634 | 0.8634   |
| 0.0395        | 4.99  | 12000 | 1.0003          | 0.8584    | 0.8584 | 0.8584 | 0.8584   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2