File size: 3,073 Bytes
744f418 |
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-PT
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-PT](https://huggingface.co/kavg/LiLT-RE-PT) on the xfun dataset.
It achieves the following results on the evaluation set:
- Precision: 0.3631
- Recall: 0.4823
- F1: 0.4143
- Loss: 0.1671
## 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 |
|:-------------:|:------:|:-----:|:---------:|:------:|:------:|:---------------:|
| 0.0907 | 41.67 | 500 | 0.3315 | 0.3106 | 0.3207 | 0.2039 |
| 0.0766 | 83.33 | 1000 | 0.3631 | 0.4823 | 0.4143 | 0.1671 |
| 0.0639 | 125.0 | 1500 | 0.3640 | 0.6086 | 0.4556 | 0.2525 |
| 0.0309 | 166.67 | 2000 | 0.3973 | 0.6010 | 0.4784 | 0.2339 |
| 0.0318 | 208.33 | 2500 | 0.4045 | 0.6414 | 0.4961 | 0.3325 |
| 0.0144 | 250.0 | 3000 | 0.4268 | 0.6187 | 0.5052 | 0.3513 |
| 0.0163 | 291.67 | 3500 | 0.4273 | 0.6086 | 0.5021 | 0.2880 |
| 0.0062 | 333.33 | 4000 | 0.4368 | 0.6288 | 0.5155 | 0.3064 |
| 0.0115 | 375.0 | 4500 | 0.4386 | 0.6313 | 0.5176 | 0.3283 |
| 0.0168 | 416.67 | 5000 | 0.4373 | 0.6162 | 0.5115 | 0.3258 |
| 0.0062 | 458.33 | 5500 | 0.4530 | 0.6086 | 0.5194 | 0.3467 |
| 0.0074 | 500.0 | 6000 | 0.4569 | 0.6162 | 0.5247 | 0.3401 |
| 0.0037 | 541.67 | 6500 | 0.4559 | 0.6136 | 0.5231 | 0.3526 |
| 0.008 | 583.33 | 7000 | 0.4650 | 0.6035 | 0.5253 | 0.3076 |
| 0.0045 | 625.0 | 7500 | 0.4610 | 0.6111 | 0.5255 | 0.3799 |
| 0.0045 | 666.67 | 8000 | 0.4551 | 0.6136 | 0.5226 | 0.3692 |
| 0.0052 | 708.33 | 8500 | 0.4535 | 0.6162 | 0.5225 | 0.3492 |
| 0.0002 | 750.0 | 9000 | 0.4537 | 0.6061 | 0.5189 | 0.4075 |
| 0.0027 | 791.67 | 9500 | 0.4581 | 0.6212 | 0.5273 | 0.3816 |
| 0.0009 | 833.33 | 10000 | 0.4569 | 0.6162 | 0.5247 | 0.3834 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
|