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---
license: mit
base_model: kavg/LiLT-RE-IT
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-IT](https://huggingface.co/kavg/LiLT-RE-IT) on the xfun dataset.
It achieves the following results on the evaluation set:
- Precision: 0.4898
- Recall: 0.6641
- F1: 0.5638
- Loss: 0.6049
## 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 |
|:-------------:|:------:|:-----:|:------:|:---------------:|:---------:|:------:|
| 0.061 | 41.67 | 500 | 0.4764 | 0.2580 | 0.4142 | 0.5606 |
| 0.0332 | 83.33 | 1000 | 0.4906 | 0.3439 | 0.4181 | 0.5934 |
| 0.0264 | 125.0 | 1500 | 0.5194 | 0.3892 | 0.4436 | 0.6263 |
| 0.0104 | 166.67 | 2000 | 0.5250 | 0.4165 | 0.4468 | 0.6364 |
| 0.0064 | 208.33 | 2500 | 0.5245 | 0.4479 | 0.4460 | 0.6364 |
| 0.0013 | 250.0 | 3000 | 0.5204 | 0.4655 | 0.4532 | 0.6111 |
| 0.0019 | 291.67 | 3500 | 0.5342 | 0.4859 | 0.4630 | 0.6313 |
| 0.0009 | 333.33 | 4000 | 0.5420 | 0.5162 | 0.4640 | 0.6515 |
| 0.0006 | 375.0 | 4500 | 0.5515 | 0.5724 | 0.4795 | 0.6490 |
| 0.0039 | 416.67 | 5000 | 0.5470 | 0.5687 | 0.4662 | 0.6616 |
| 0.0012 | 458.33 | 5500 | 0.5595 | 0.5582 | 0.4860 | 0.6591 |
| 0.0001 | 500.0 | 6000 | 0.5730 | 0.5709 | 0.4981 | 0.6742 |
| 0.0022 | 541.67 | 6500 | 0.5578 | 0.5795 | 0.4877 | 0.6515 |
| 0.0012 | 583.33 | 7000 | 0.5674 | 0.5710 | 0.4953 | 0.6641 |
| 0.0009 | 625.0 | 7500 | 0.5607 | 0.5994 | 0.4879 | 0.6591 |
| 0.0002 | 666.67 | 8000 | 0.5616 | 0.5865 | 0.4879 | 0.6616 |
| 0.0016 | 708.33 | 8500 | 0.4972 | 0.6717 | 0.5714 | 0.5878 |
| 0.0 | 750.0 | 9000 | 0.4898 | 0.6641 | 0.5638 | 0.6049 |
| 0.0002 | 791.67 | 9500 | 0.4826 | 0.6641 | 0.5590 | 0.6223 |
| 0.0014 | 833.33 | 10000 | 0.4890 | 0.6742 | 0.5669 | 0.6318 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
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