---
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: 0.2809
- Recall: 0.5051
- F1: 0.3610
- Loss: 1.6168

## 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: 8000

### Training results

| Training Loss | Epoch  | Step | F1     | Validation Loss | Precision | Recall |
|:-------------:|:------:|:----:|:------:|:---------------:|:---------:|:------:|
| 0.1546        | 41.67  | 500  | 0      | 0.2482          | 0         | 0      |
| 0.1674        | 83.33  | 1000 | 0      | 0.2477          | 0         | 0      |
| 0.1368        | 125.0  | 1500 | 0.1502 | 0.2256          | 0.1975    | 0.1212 |
| 0.0727        | 166.67 | 2000 | 0.2732 | 0.3218          | 0.2091    | 0.3939 |
| 0.0718        | 208.33 | 2500 | 0.3385 | 0.3518          | 0.2579    | 0.4924 |
| 0.0612        | 250.0  | 3000 | 0.3371 | 0.5235          | 0.2555    | 0.4949 |
| 0.0504        | 291.67 | 3500 | 0.3353 | 0.5280          | 0.2536    | 0.4949 |
| 0.0418        | 333.33 | 4000 | 0.3476 | 0.6919          | 0.2657    | 0.5025 |
| 0.0308        | 375.0  | 4500 | 0.3490 | 0.7819          | 0.2613    | 0.5253 |
| 0.039         | 416.67 | 5000 | 0.3463 | 1.0291          | 0.2627    | 0.5076 |
| 0.0301        | 458.33 | 5500 | 0.3443 | 1.1661          | 0.2626    | 0.5    |
| 0.0245        | 500.0  | 6000 | 0.3414 | 1.2341          | 0.2642    | 0.4823 |
| 0.0347        | 541.67 | 6500 | 0.3389 | 1.4114          | 0.2605    | 0.4848 |
| 0.0327        | 583.33 | 7000 | 0.3422 | 1.4326          | 0.2683    | 0.4722 |
| 0.0117        | 625.0  | 7500 | 0.3670 | 1.6092          | 0.2899    | 0.5    |
| 0.0255        | 666.67 | 8000 | 0.3607 | 1.6141          | 0.2805    | 0.5051 |


### Framework versions

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