---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: lilt-xlm-roberta-base-finetuned-DocLayNet-base_paragraphs_ml512-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. -->

# lilt-xlm-roberta-base-finetuned-DocLayNet-base_paragraphs_ml512-v5

This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4104
- Precision: 0.8634
- Recall: 0.8634
- F1: 0.8634
- Accuracy: 0.8634

## 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
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.05  | 100  | 0.9875          | 0.6585    | 0.6585 | 0.6585 | 0.6585   |
| No log        | 0.11  | 200  | 0.7886          | 0.7551    | 0.7551 | 0.7551 | 0.7551   |
| No log        | 0.16  | 300  | 0.5894          | 0.8248    | 0.8248 | 0.8248 | 0.8248   |
| No log        | 0.21  | 400  | 0.4794          | 0.8396    | 0.8396 | 0.8396 | 0.8396   |
| 0.7446        | 0.27  | 500  | 0.3993          | 0.8703    | 0.8703 | 0.8703 | 0.8703   |
| 0.7446        | 0.32  | 600  | 0.3631          | 0.8857    | 0.8857 | 0.8857 | 0.8857   |
| 0.7446        | 0.37  | 700  | 0.4096          | 0.8630    | 0.8630 | 0.8630 | 0.8630   |
| 0.7446        | 0.43  | 800  | 0.4492          | 0.8528    | 0.8528 | 0.8528 | 0.8528   |
| 0.7446        | 0.48  | 900  | 0.3839          | 0.8834    | 0.8834 | 0.8834 | 0.8834   |
| 0.4464        | 0.53  | 1000 | 0.4365          | 0.8498    | 0.8498 | 0.8498 | 0.8498   |
| 0.4464        | 0.59  | 1100 | 0.3616          | 0.8812    | 0.8812 | 0.8812 | 0.8812   |
| 0.4464        | 0.64  | 1200 | 0.3949          | 0.8796    | 0.8796 | 0.8796 | 0.8796   |
| 0.4464        | 0.69  | 1300 | 0.4184          | 0.8613    | 0.8613 | 0.8613 | 0.8613   |
| 0.4464        | 0.75  | 1400 | 0.4130          | 0.8743    | 0.8743 | 0.8743 | 0.8743   |
| 0.3672        | 0.8   | 1500 | 0.4535          | 0.8289    | 0.8289 | 0.8289 | 0.8289   |
| 0.3672        | 0.85  | 1600 | 0.3681          | 0.8713    | 0.8713 | 0.8713 | 0.8713   |
| 0.3672        | 0.91  | 1700 | 0.3446          | 0.8857    | 0.8857 | 0.8857 | 0.8857   |
| 0.3672        | 0.96  | 1800 | 0.4104          | 0.8634    | 0.8634 | 0.8634 | 0.8634   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2