---
license: mit
base_model: nielsr/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 [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: 0.4110
- Precision: 0.8507
- Recall: 0.8507
- F1: 0.8507
- Accuracy: 0.8507

## 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        | 0.0533 | 100  | 0.9209          | 0.6685    | 0.6685 | 0.6685 | 0.6685   |
| No log        | 0.1066 | 200  | 0.6489          | 0.8154    | 0.8154 | 0.8154 | 0.8154   |
| No log        | 0.1599 | 300  | 0.7198          | 0.7420    | 0.7420 | 0.7420 | 0.7420   |
| No log        | 0.2132 | 400  | 0.5487          | 0.7961    | 0.7961 | 0.7961 | 0.7961   |
| 0.736         | 0.2665 | 500  | 0.4430          | 0.8669    | 0.8669 | 0.8669 | 0.8669   |
| 0.736         | 0.3198 | 600  | 0.4406          | 0.8614    | 0.8614 | 0.8614 | 0.8614   |
| 0.736         | 0.3731 | 700  | 0.4112          | 0.8651    | 0.8651 | 0.8651 | 0.8651   |
| 0.736         | 0.4264 | 800  | 0.4101          | 0.8711    | 0.8711 | 0.8711 | 0.8711   |
| 0.736         | 0.4797 | 900  | 0.4691          | 0.8398    | 0.8398 | 0.8398 | 0.8398   |
| 0.4575        | 0.5330 | 1000 | 0.4894          | 0.8009    | 0.8009 | 0.8009 | 0.8009   |
| 0.4575        | 0.5864 | 1100 | 0.4275          | 0.8553    | 0.8553 | 0.8553 | 0.8553   |
| 0.4575        | 0.6397 | 1200 | 0.3614          | 0.8857    | 0.8857 | 0.8857 | 0.8857   |
| 0.4575        | 0.6930 | 1300 | 0.4571          | 0.8432    | 0.8432 | 0.8432 | 0.8432   |
| 0.4575        | 0.7463 | 1400 | 0.4478          | 0.8377    | 0.8377 | 0.8377 | 0.8377   |
| 0.3718        | 0.7996 | 1500 | 0.4381          | 0.8324    | 0.8324 | 0.8324 | 0.8324   |
| 0.3718        | 0.8529 | 1600 | 0.3267          | 0.8963    | 0.8963 | 0.8963 | 0.8963   |
| 0.3718        | 0.9062 | 1700 | 0.3470          | 0.8890    | 0.8890 | 0.8890 | 0.8890   |
| 0.3718        | 0.9595 | 1800 | 0.4110          | 0.8507    | 0.8507 | 0.8507 | 0.8507   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1