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

# lilt-xlm-roberta-base-finetuned-DocLayNet-base_paragraphs_ml512-v1

This model is a fine-tuned version of [zwaarcontrast/lilt-xlm-roberta-base](https://huggingface.co/zwaarcontrast/lilt-xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0645
- Precision: 0.8277
- Recall: 0.8277
- F1: 0.8277
- Accuracy: 0.8277

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.05  | 100  | 0.1162          | 0.6875    | 0.6875 | 0.6875 | 0.6875   |
| No log        | 0.11  | 200  | 0.1097          | 0.6755    | 0.6755 | 0.6755 | 0.6755   |
| No log        | 0.16  | 300  | 0.0866          | 0.7781    | 0.7781 | 0.7781 | 0.7781   |
| No log        | 0.21  | 400  | 0.1018          | 0.7478    | 0.7478 | 0.7478 | 0.7478   |
| 0.0975        | 0.27  | 500  | 0.0645          | 0.8277    | 0.8277 | 0.8277 | 0.8277   |
| 0.0975        | 0.32  | 600  | 0.0767          | 0.7985    | 0.7985 | 0.7985 | 0.7985   |
| 0.0975        | 0.37  | 700  | 0.0758          | 0.7903    | 0.7903 | 0.7903 | 0.7903   |
| 0.0975        | 0.43  | 800  | 0.0862          | 0.7865    | 0.7865 | 0.7865 | 0.7865   |
| 0.0975        | 0.48  | 900  | 0.1243          | 0.6892    | 0.6892 | 0.6892 | 0.6892   |
| 0.1389        | 0.53  | 1000 | 0.0795          | 0.8256    | 0.8256 | 0.8256 | 0.8256   |
| 0.1389        | 0.59  | 1100 | 0.1683          | 0.4702    | 0.4702 | 0.4702 | 0.4702   |
| 0.1389        | 0.64  | 1200 | 0.1253          | 0.6636    | 0.6636 | 0.6636 | 0.6636   |
| 0.1389        | 0.69  | 1300 | 0.1044          | 0.7295    | 0.7295 | 0.7295 | 0.7295   |
| 0.1389        | 0.75  | 1400 | 0.1235          | 0.6477    | 0.6477 | 0.6477 | 0.6477   |
| 0.1018        | 0.8   | 1500 | 0.1113          | 0.7276    | 0.7276 | 0.7276 | 0.7276   |
| 0.1018        | 0.85  | 1600 | 0.1014          | 0.7317    | 0.7317 | 0.7317 | 0.7317   |
| 0.1018        | 0.91  | 1700 | 0.0963          | 0.7293    | 0.7293 | 0.7293 | 0.7293   |
| 0.1018        | 0.96  | 1800 | 0.1012          | 0.7323    | 0.7323 | 0.7323 | 0.7323   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1