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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: 0.3054
- 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
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