---
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- funsd_re
metrics:
- precision
- recall
- f1
model-index:
- name: checkpoints
  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. -->

# checkpoints

This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on the funsd_re dataset.
It achieves the following results on the evaluation set:
- Precision: 0.3264
- Recall: 0.4864
- F1: 0.3907
- Loss: 0.4377

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 5000

### Training results

| Training Loss | Epoch  | Step | F1     | Validation Loss | Precision | Recall |
|:-------------:|:------:|:----:|:------:|:---------------:|:---------:|:------:|
| 0.1604        | 26.32  | 500  | 0      | 0.1513          | 0         | 0      |
| 0.1012        | 52.63  | 1000 | 0.0098 | 0.0786          | 0.5       | 0.0049 |
| 0.0994        | 78.95  | 1500 | 0.2518 | 0.1847          | 0.3729    | 0.1901 |
| 0.0694        | 105.26 | 2000 | 0.3499 | 0.1926          | 0.3667    | 0.3346 |
| 0.0771        | 131.58 | 2500 | 0.3856 | 0.3295          | 0.3450    | 0.4370 |
| 0.0565        | 157.89 | 3000 | 0.3865 | 0.4137          | 0.3293    | 0.4679 |
| 0.0411        | 184.21 | 3500 | 0.3808 | 0.3624          | 0.3252    | 0.4593 |
| 0.0463        | 210.53 | 4000 | 0.3832 | 0.5089          | 0.3221    | 0.4728 |
| 0.0414        | 236.84 | 4500 | 0.3911 | 0.6137          | 0.3305    | 0.4790 |
| 0.036         | 263.16 | 5000 | 0.3910 | 0.4428          | 0.3275    | 0.4852 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1