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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: 0.3911
- Recall: 0.6703
- F1: 0.4940
- Loss: 0.1352
## 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: 6
- 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 |
|:-------------:|:------:|:-----:|:------:|:---------------:|:---------:|:------:|
| 0.1469 | 20.83 | 500 | 0 | 0.1467 | 0 | 0 |
| 0.0896 | 41.67 | 1000 | 0.0837 | 0.1454 | 0.2946 | 0.0487 |
| 0.1027 | 62.5 | 1500 | 0.1225 | 0.1353 | 0.3333 | 0.0750 |
| 0.0485 | 83.33 | 2000 | 0.3536 | 0.1571 | 0.3364 | 0.3727 |
| 0.0597 | 104.17 | 2500 | 0.4448 | 0.1546 | 0.3535 | 0.5997 |
| 0.0367 | 125.0 | 3000 | 0.4940 | 0.1352 | 0.3911 | 0.6703 |
| 0.033 | 145.83 | 3500 | 0.4977 | 0.1749 | 0.3902 | 0.6870 |
| 0.0176 | 166.67 | 4000 | 0.5087 | 0.2262 | 0.4034 | 0.6883 |
| 0.0123 | 187.5 | 4500 | 0.5050 | 0.2358 | 0.3978 | 0.6915 |
| 0.0194 | 208.33 | 5000 | 0.5173 | 0.2976 | 0.4090 | 0.7037 |
| 0.0118 | 171.88 | 5500 | 0.4159 | 0.6863 | 0.5179 | 0.2836 |
| 0.0054 | 187.5 | 6000 | 0.4356 | 0.6703 | 0.5280 | 0.3100 |
| 0.01 | 203.12 | 6500 | 0.4229 | 0.6979 | 0.5266 | 0.3430 |
| 0.0062 | 218.75 | 7000 | 0.4272 | 0.7062 | 0.5324 | 0.3652 |
| 0.0051 | 234.38 | 7500 | 0.4306 | 0.6947 | 0.5317 | 0.3496 |
| 0.0048 | 250.0 | 8000 | 0.4400 | 0.6940 | 0.5386 | 0.3943 |
| 0.0087 | 265.62 | 8500 | 0.4290 | 0.6992 | 0.5317 | 0.3782 |
| 0.0077 | 281.25 | 9000 | 0.4394 | 0.7049 | 0.5414 | 0.3855 |
| 0.0014 | 296.88 | 9500 | 0.4363 | 0.7004 | 0.5377 | 0.3933 |
| 0.0035 | 312.5 | 10000 | 0.4350 | 0.6992 | 0.5363 | 0.4045 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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