transformers pytorch lilt token-classification generated_from_trainer base_model:nielsr/lilt-xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us
---
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LILT_DocLayNet_Large
  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_DocLayNet_Large

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: 0.4755
- Precision: 0.8941
- Recall: 0.8941
- F1: 0.8941
- Accuracy: 0.8941

## 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: 16
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.427         | 0.05  | 1000  | 0.5688          | 0.8324    | 0.8324 | 0.8324 | 0.8324   |
| 0.3433        | 0.11  | 2000  | 0.4321          | 0.8730    | 0.8730 | 0.8730 | 0.8730   |
| 0.2772        | 0.16  | 3000  | 0.5145          | 0.8764    | 0.8764 | 0.8764 | 0.8764   |
| 0.285         | 0.21  | 4000  | 0.4071          | 0.8843    | 0.8843 | 0.8843 | 0.8843   |
| 0.2704        | 0.27  | 5000  | 0.4022          | 0.8747    | 0.8747 | 0.8747 | 0.8747   |
| 0.2268        | 0.32  | 6000  | 0.4882          | 0.8732    | 0.8732 | 0.8732 | 0.8732   |
| 0.2152        | 0.37  | 7000  | 0.5419          | 0.8844    | 0.8844 | 0.8844 | 0.8844   |
| 0.2172        | 0.42  | 8000  | 0.5472          | 0.8793    | 0.8793 | 0.8793 | 0.8793   |
| 0.2692        | 0.48  | 9000  | 0.6007          | 0.8790    | 0.8790 | 0.8790 | 0.8790   |
| 0.2264        | 0.53  | 10000 | 0.7257          | 0.8793    | 0.8793 | 0.8793 | 0.8793   |
| 0.2243        | 0.58  | 11000 | 0.5470          | 0.8882    | 0.8882 | 0.8882 | 0.8882   |
| 0.1898        | 0.64  | 12000 | 0.6281          | 0.8850    | 0.8850 | 0.8850 | 0.8850   |
| 0.2037        | 0.69  | 13000 | 0.5516          | 0.8913    | 0.8913 | 0.8913 | 0.8913   |
| 0.1935        | 0.74  | 14000 | 0.5198          | 0.8859    | 0.8859 | 0.8859 | 0.8859   |
| 0.2057        | 0.8   | 15000 | 0.5371          | 0.8915    | 0.8915 | 0.8915 | 0.8915   |
| 0.2233        | 0.85  | 16000 | 0.5197          | 0.8835    | 0.8835 | 0.8835 | 0.8835   |
| 0.1597        | 0.9   | 17000 | 0.4827          | 0.8934    | 0.8934 | 0.8934 | 0.8934   |
| 0.2133        | 0.96  | 18000 | 0.4755          | 0.8941    | 0.8941 | 0.8941 | 0.8941   |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
