|
--- |
|
license: mit |
|
base_model: kavg/LiLT-RE-ES |
|
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-ES](https://huggingface.co/kavg/LiLT-RE-ES) on the xfun dataset. |
|
It achieves the following results on the evaluation set: |
|
- Precision: 0.2886 |
|
- Recall: 0.3586 |
|
- F1: 0.3198 |
|
- Loss: 0.2312 |
|
|
|
## 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 | |
|
|:-------------:|:------:|:-----:|:---------:|:------:|:------:|:---------------:| |
|
| 0.1103 | 41.67 | 500 | 0.4808 | 0.0631 | 0.1116 | 0.2442 | |
|
| 0.0871 | 83.33 | 1000 | 0.2886 | 0.3586 | 0.3198 | 0.2312 | |
|
| 0.0905 | 125.0 | 1500 | 0.2904 | 0.5177 | 0.3721 | 0.2402 | |
|
| 0.0521 | 166.67 | 2000 | 0.3065 | 0.5581 | 0.3957 | 0.2793 | |
|
| 0.0508 | 208.33 | 2500 | 0.3080 | 0.6136 | 0.4101 | 0.4084 | |
|
| 0.0509 | 250.0 | 3000 | 0.3250 | 0.5934 | 0.4200 | 0.4008 | |
|
| 0.0406 | 291.67 | 3500 | 0.3290 | 0.5808 | 0.4201 | 0.4593 | |
|
| 0.0333 | 333.33 | 4000 | 0.3488 | 0.5884 | 0.4380 | 0.4806 | |
|
| 0.0358 | 375.0 | 4500 | 0.3456 | 0.5682 | 0.4298 | 0.6472 | |
|
| 0.0289 | 416.67 | 5000 | 0.3657 | 0.5808 | 0.4488 | 0.6532 | |
|
| 0.0255 | 458.33 | 5500 | 0.3601 | 0.5783 | 0.4438 | 0.7617 | |
|
| 0.0183 | 500.0 | 6000 | 0.3736 | 0.5859 | 0.4562 | 0.7025 | |
|
| 0.0213 | 541.67 | 6500 | 0.3606 | 0.5783 | 0.4442 | 0.8442 | |
|
| 0.0296 | 583.33 | 7000 | 0.3621 | 0.5505 | 0.4369 | 0.7416 | |
|
| 0.0418 | 625.0 | 7500 | 0.3659 | 0.5682 | 0.4451 | 0.7372 | |
|
| 0.0225 | 666.67 | 8000 | 0.3729 | 0.5556 | 0.4462 | 0.8660 | |
|
| 0.0225 | 708.33 | 8500 | 0.3723 | 0.5707 | 0.4506 | 0.8646 | |
|
| 0.0128 | 750.0 | 9000 | 0.375 | 0.5606 | 0.4494 | 0.7905 | |
|
| 0.0182 | 791.67 | 9500 | 0.3758 | 0.5657 | 0.4516 | 0.8551 | |
|
| 0.0061 | 833.33 | 10000 | 0.3788 | 0.5606 | 0.4521 | 0.8355 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.1 |
|
|
