---
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: 0.3111
- Recall: 0.5225
- F1: 0.3900
- Loss: 0.1579

## 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  | F1     | Validation Loss | Precision | Recall |
|:-------------:|:------:|:-----:|:------:|:---------------:|:---------:|:------:|
| 0.2077        | 16.13  | 500   | 0      | 0.2127          | 0         | 0      |
| 0.1792        | 32.26  | 1000  | 0.2520 | 0.1668          | 0.2345    | 0.2723 |
| 0.1063        | 48.39  | 1500  | 0.2491 | 0.1439          | 0.5851    | 0.1582 |
| 0.1147        | 64.52  | 2000  | 0.3900 | 0.1579          | 0.3111    | 0.5225 |
| 0.0718        | 80.65  | 2500  | 0.4216 | 0.2598          | 0.3328    | 0.5753 |
| 0.0503        | 96.77  | 3000  | 0.4471 | 0.1888          | 0.3563    | 0.6002 |
| 0.0823        | 112.9  | 3500  | 0.4302 | 0.2690          | 0.3157    | 0.6750 |
| 0.0586        | 129.03 | 4000  | 0.4360 | 0.2429          | 0.3211    | 0.6788 |
| 0.0604        | 145.16 | 4500  | 0.4578 | 0.2745          | 0.3503    | 0.6606 |
| 0.0603        | 161.29 | 5000  | 0.4630 | 0.2694          | 0.3483    | 0.6903 |
| 0.0434        | 177.42 | 5500  | 0.4575 | 0.3200          | 0.3417    | 0.6922 |
| 0.0367        | 193.55 | 6000  | 0.4523 | 0.2991          | 0.3321    | 0.7085 |
| 0.0402        | 209.68 | 6500  | 0.4664 | 0.2628          | 0.3507    | 0.6961 |
| 0.027         | 225.81 | 7000  | 0.4671 | 0.3375          | 0.3495    | 0.7037 |
| 0.0363        | 241.94 | 7500  | 0.3445 | 0.7018          | 0.4621    | 0.3380 |
| 0.0411        | 258.06 | 8000  | 0.3641 | 0.6769          | 0.4735    | 0.2984 |
| 0.0348        | 274.19 | 8500  | 0.3530 | 0.6951          | 0.4682    | 0.3455 |
| 0.0031        | 290.32 | 9000  | 0.3510 | 0.6999          | 0.4675    | 0.3841 |
| 0.0259        | 306.45 | 9500  | 0.3532 | 0.6989          | 0.4693    | 0.3586 |
| 0.0129        | 322.58 | 10000 | 0.3513 | 0.7009          | 0.4680    | 0.3604 |


### Framework versions

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