File size: 3,073 Bytes
9d2565a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
---
license: mit
base_model: kavg/LiLT-RE-FR
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-FR](https://huggingface.co/kavg/LiLT-RE-FR) on the xfun dataset.
It achieves the following results on the evaluation set:
- Precision: 0.3604
- Recall: 0.5707
- F1: 0.4418
- Loss: 0.2693
## 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 |
|:-------------:|:------:|:-----:|:---------:|:------:|:------:|:---------------:|
| 0.0868 | 41.67 | 500 | 0.3688 | 0.2803 | 0.3185 | 0.1679 |
| 0.0557 | 83.33 | 1000 | 0.3604 | 0.5707 | 0.4418 | 0.2693 |
| 0.0513 | 125.0 | 1500 | 0.3962 | 0.5833 | 0.4719 | 0.3008 |
| 0.0248 | 166.67 | 2000 | 0.4043 | 0.6237 | 0.4906 | 0.4857 |
| 0.0139 | 208.33 | 2500 | 0.4296 | 0.6010 | 0.5011 | 0.4227 |
| 0.004 | 250.0 | 3000 | 0.4177 | 0.6212 | 0.4995 | 0.5369 |
| 0.0084 | 291.67 | 3500 | 0.4255 | 0.6490 | 0.514 | 0.5332 |
| 0.0067 | 333.33 | 4000 | 0.4259 | 0.6389 | 0.5111 | 0.4978 |
| 0.0008 | 375.0 | 4500 | 0.4189 | 0.6263 | 0.5020 | 0.4567 |
| 0.0116 | 416.67 | 5000 | 0.4336 | 0.6515 | 0.5207 | 0.5514 |
| 0.0007 | 458.33 | 5500 | 0.4394 | 0.6414 | 0.5216 | 0.5703 |
| 0.0004 | 500.0 | 6000 | 0.4504 | 0.6540 | 0.5335 | 0.6107 |
| 0.002 | 541.67 | 6500 | 0.4480 | 0.6414 | 0.5275 | 0.5859 |
| 0.0059 | 583.33 | 7000 | 0.4526 | 0.6263 | 0.5254 | 0.6033 |
| 0.0023 | 625.0 | 7500 | 0.4379 | 0.6414 | 0.5205 | 0.6440 |
| 0.0007 | 666.67 | 8000 | 0.4499 | 0.6237 | 0.5228 | 0.5594 |
| 0.003 | 708.33 | 8500 | 0.4393 | 0.6490 | 0.5240 | 0.6276 |
| 0.0001 | 750.0 | 9000 | 0.4410 | 0.6515 | 0.5260 | 0.6132 |
| 0.001 | 791.67 | 9500 | 0.4376 | 0.6288 | 0.5161 | 0.6312 |
| 0.0001 | 833.33 | 10000 | 0.4415 | 0.6389 | 0.5222 | 0.6304 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
|