---
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xfun
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 xfun dataset.
It achieves the following results on the evaluation set:
- Precision: 0.2199
- Recall: 0.5308
- F1: 0.3109
- Loss: 0.1355

## 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: 10000

### Training results

| Training Loss | Epoch  | Step  | Precision | Recall | F1     | Validation Loss |
|:-------------:|:------:|:-----:|:---------:|:------:|:------:|:---------------:|
| 0.1811        | 16.67  | 500   | 0         | 0      | 0      | 0.2498          |
| 0.1861        | 33.33  | 1000  | 0.4207    | 0.0457 | 0.0825 | 0.2486          |
| 0.0373        | 50.0   | 1500  | 0.4845    | 0.0517 | 0.0934 | 0.2116          |
| 0.0856        | 66.67  | 2000  | 0.3184    | 0.1504 | 0.2043 | 0.1696          |
| 0.0867        | 83.33  | 2500  | 0.2201    | 0.3472 | 0.2694 | 0.1691          |
| 0.0832        | 100.0  | 3000  | 0.2369    | 0.3685 | 0.2884 | 0.1687          |
| 0.0756        | 116.67 | 3500  | 0.2152    | 0.4301 | 0.2869 | 0.1561          |
| 0.0454        | 133.33 | 4000  | 0.2075    | 0.4818 | 0.2900 | 0.1513          |
| 0.0629        | 150.0  | 4500  | 0.2202    | 0.5282 | 0.3108 | 0.1748          |
| 0.0503        | 166.67 | 5000  | 0.2058    | 0.5573 | 0.3006 | 0.1832          |
| 0.05          | 183.33 | 5500  | 0.2263    | 0.5381 | 0.3186 | 0.1863          |
| 0.0365        | 200.0  | 6000  | 0.2233    | 0.5712 | 0.3211 | 0.1524          |
| 0.0366        | 216.67 | 6500  | 0.2219    | 0.5699 | 0.3195 | 0.1254          |
| 0.0352        | 233.33 | 7000  | 0.2199    | 0.5308 | 0.3109 | 0.1355          |
| 0.0524        | 250.0  | 7500  | 0.2184    | 0.5500 | 0.3126 | 0.1456          |
| 0.0375        | 266.67 | 8000  | 0.2158    | 0.5401 | 0.3084 | 0.1549          |
| 0.0324        | 283.33 | 8500  | 0.2068    | 0.5507 | 0.3007 | 0.1840          |
| 0.0168        | 300.0  | 9000  | 0.2155    | 0.5520 | 0.3100 | 0.1686          |
| 0.0306        | 316.67 | 9500  | 0.2160    | 0.5666 | 0.3128 | 0.1558          |
| 0.0229        | 333.33 | 10000 | 0.2152    | 0.5712 | 0.3126 | 0.1633          |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1