---
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: 0.3126
- Recall: 0.6777
- F1: 0.4278
- Loss: 0.5651

## 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 |
|:-------------:|:------:|:-----:|:------:|:---------------:|:---------:|:------:|
| 0.2058        | 19.23  | 500   | 0      | 0.2763          | 0         | 0      |
| 0.145         | 38.46  | 1000  | 0.0623 | 0.2325          | 0.2889    | 0.0349 |
| 0.1441        | 57.69  | 1500  | 0.1232 | 0.2306          | 0.2616    | 0.0806 |
| 0.0902        | 76.92  | 2000  | 0.2645 | 0.2439          | 0.2526    | 0.2775 |
| 0.0768        | 96.15  | 2500  | 0.3176 | 0.3033          | 0.2440    | 0.4548 |
| 0.0707        | 115.38 | 3000  | 0.3472 | 0.3333          | 0.2778    | 0.4628 |
| 0.0649        | 134.62 | 3500  | 0.3509 | 0.3677          | 0.2629    | 0.5273 |
| 0.0257        | 153.85 | 4000  | 0.3705 | 0.4219          | 0.2810    | 0.5434 |
| 0.054         | 173.08 | 4500  | 0.3699 | 0.4440          | 0.2729    | 0.5739 |
| 0.0368        | 192.31 | 5000  | 0.3942 | 0.4843          | 0.3005    | 0.5730 |
| 0.0326        | 211.54 | 5500  | 0.3968 | 0.4651          | 0.2952    | 0.6052 |
| 0.0412        | 230.77 | 6000  | 0.4100 | 0.5386          | 0.3018    | 0.6392 |
| 0.0603        | 250.0  | 6500  | 0.4189 | 0.4957          | 0.3068    | 0.6598 |
| 0.0215        | 269.23 | 7000  | 0.4127 | 0.4768          | 0.2999    | 0.6616 |
| 0.0233        | 288.46 | 7500  | 0.4284 | 0.5245          | 0.3183    | 0.6553 |
| 0.0212        | 307.69 | 8000  | 0.4259 | 0.5424          | 0.3091    | 0.6849 |
| 0.0152        | 326.92 | 8500  | 0.4206 | 0.5655          | 0.3073    | 0.6661 |
| 0.0147        | 346.15 | 9000  | 0.4260 | 0.5630          | 0.3123    | 0.6697 |
| 0.0205        | 365.38 | 9500  | 0.4321 | 0.5389          | 0.3174    | 0.6768 |
| 0.0115        | 384.62 | 10000 | 0.4278 | 0.5651          | 0.3126    | 0.6777 |


### Framework versions

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