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---
license: mit
base_model: kavg/LiLT-RE-DE
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 [kavg/LiLT-RE-DE](https://huggingface.co/kavg/LiLT-RE-DE) on the xfun dataset.
It achieves the following results on the evaluation set:
- Precision: 0.2952
- Recall: 0.4167
- F1: 0.3455
- Loss: 0.3186
## 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 |
|:-------------:|:------:|:-----:|:---------:|:------:|:------:|:---------------:|
| 0.1035 | 41.67 | 500 | 0.2905 | 0.1540 | 0.2013 | 0.2291 |
| 0.0691 | 83.33 | 1000 | 0.2952 | 0.4167 | 0.3455 | 0.3186 |
| 0.0442 | 125.0 | 1500 | 0.2970 | 0.5909 | 0.3953 | 0.2765 |
| 0.024 | 166.67 | 2000 | 0.3227 | 0.5884 | 0.4168 | 0.4144 |
| 0.0216 | 208.33 | 2500 | 0.3234 | 0.6035 | 0.4211 | 0.4036 |
| 0.0096 | 250.0 | 3000 | 0.3534 | 0.6364 | 0.4545 | 0.5716 |
| 0.0079 | 291.67 | 3500 | 0.3456 | 0.5934 | 0.4368 | 0.6643 |
| 0.0045 | 333.33 | 4000 | 0.3427 | 0.6187 | 0.4410 | 0.6955 |
| 0.0017 | 375.0 | 4500 | 0.3587 | 0.6187 | 0.4541 | 0.8144 |
| 0.0147 | 416.67 | 5000 | 0.3407 | 0.6212 | 0.4401 | 0.8101 |
| 0.0027 | 458.33 | 5500 | 0.3491 | 0.6162 | 0.4457 | 0.8809 |
| 0.0079 | 500.0 | 6000 | 0.3183 | 0.6061 | 0.4174 | 0.8863 |
| 0.0028 | 541.67 | 6500 | 0.3506 | 0.5985 | 0.4422 | 0.9944 |
| 0.0075 | 583.33 | 7000 | 0.3476 | 0.5960 | 0.4391 | 0.9920 |
| 0.0002 | 625.0 | 7500 | 0.3448 | 0.6061 | 0.4396 | 0.9752 |
| 0.0025 | 666.67 | 8000 | 0.3456 | 0.6162 | 0.4428 | 0.9866 |
| 0.0037 | 708.33 | 8500 | 0.3465 | 0.6187 | 0.4442 | 1.0153 |
| 0.0041 | 750.0 | 9000 | 0.3442 | 0.6136 | 0.4410 | 1.1227 |
| 0.0023 | 791.67 | 9500 | 0.3450 | 0.6237 | 0.4442 | 1.0995 |
| 0.0007 | 833.33 | 10000 | 0.3408 | 0.6162 | 0.4388 | 1.1097 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
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