---
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: 0.3054
- Recall: 0.6032
- F1: 0.4055
- Loss: 0.2164

## 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 |
|:-------------:|:------:|:-----:|:---------:|:------:|:------:|:---------------:|
| 0.1914        | 20.83  | 500   | 0         | 0      | 0      | 0.2039          |
| 0.1638        | 41.67  | 1000  | 0.4688    | 0.0252 | 0.0478 | 0.2196          |
| 0.0928        | 62.5   | 1500  | 0.3790    | 0.1669 | 0.2318 | 0.2127          |
| 0.0948        | 83.33  | 2000  | 0.3125    | 0.4245 | 0.3600 | 0.2987          |
| 0.0796        | 104.17 | 2500  | 0.3102    | 0.5587 | 0.3989 | 0.3636          |
| 0.0469        | 125.0  | 3000  | 0.3204    | 0.5134 | 0.3946 | 0.3587          |
| 0.0471        | 145.83 | 3500  | 0.3303    | 0.5243 | 0.4053 | 0.2792          |
| 0.0486        | 166.67 | 4000  | 0.2967    | 0.5973 | 0.3964 | 0.2973          |
| 0.0381        | 187.5  | 4500  | 0.3066    | 0.6007 | 0.4060 | 0.3003          |
| 0.0392        | 208.33 | 5000  | 0.3054    | 0.6032 | 0.4055 | 0.2164          |
| 0.0268        | 229.17 | 5500  | 0.3052    | 0.6158 | 0.4081 | 0.3159          |
| 0.029         | 250.0  | 6000  | 0.2850    | 0.6292 | 0.3923 | 0.3108          |
| 0.0217        | 270.83 | 6500  | 0.2964    | 0.6141 | 0.3998 | 0.3130          |
| 0.0241        | 291.67 | 7000  | 0.3012    | 0.6216 | 0.4058 | 0.3197          |
| 0.038         | 312.5  | 7500  | 0.3051    | 0.6216 | 0.4093 | 0.2627          |
| 0.0374        | 333.33 | 8000  | 0.2914    | 0.6359 | 0.3997 | 0.3388          |
| 0.0194        | 354.17 | 8500  | 0.2975    | 0.6275 | 0.4037 | 0.3155          |
| 0.0189        | 375.0  | 9000  | 0.3037    | 0.625  | 0.4088 | 0.2911          |
| 0.0147        | 395.83 | 9500  | 0.2993    | 0.6242 | 0.4046 | 0.3417          |
| 0.0328        | 416.67 | 10000 | 0.3012    | 0.6242 | 0.4063 | 0.3210          |


### Framework versions

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