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---
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
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