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--- |
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license: mit |
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base_model: kavg/LiLT-RE-JA |
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tags: |
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- generated_from_trainer |
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datasets: |
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- xfun |
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metrics: |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: checkpoints |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# checkpoints |
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This model is a fine-tuned version of [kavg/LiLT-RE-JA](https://huggingface.co/kavg/LiLT-RE-JA) on the xfun dataset. |
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It achieves the following results on the evaluation set: |
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- Precision: 0.4744 |
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- Recall: 0.6540 |
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- F1: 0.5499 |
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- Loss: 0.5293 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | F1 | Validation Loss | Precision | Recall | |
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|:-------------:|:------:|:-----:|:------:|:---------------:|:---------:|:------:| |
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| 0.0815 | 41.67 | 500 | 0.4149 | 0.1502 | 0.3521 | 0.5051 | |
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| 0.0408 | 83.33 | 1000 | 0.4931 | 0.1593 | 0.4244 | 0.5884 | |
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| 0.0435 | 125.0 | 1500 | 0.5041 | 0.2311 | 0.4218 | 0.6263 | |
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| 0.0168 | 166.67 | 2000 | 0.5097 | 0.3195 | 0.4286 | 0.6288 | |
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| 0.0073 | 208.33 | 2500 | 0.5088 | 0.3313 | 0.4308 | 0.6212 | |
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| 0.0051 | 250.0 | 3000 | 0.5264 | 0.3939 | 0.4349 | 0.6667 | |
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| 0.0038 | 291.67 | 3500 | 0.5252 | 0.3958 | 0.4435 | 0.6439 | |
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| 0.0016 | 333.33 | 4000 | 0.5335 | 0.4708 | 0.4606 | 0.6338 | |
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| 0.0082 | 375.0 | 4500 | 0.5340 | 0.4429 | 0.4562 | 0.6439 | |
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| 0.0079 | 416.67 | 5000 | 0.5305 | 0.4498 | 0.4601 | 0.6263 | |
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| 0.0028 | 458.33 | 5500 | 0.5352 | 0.4993 | 0.4578 | 0.6439 | |
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| 0.0003 | 500.0 | 6000 | 0.5422 | 0.5253 | 0.4695 | 0.6414 | |
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| 0.0014 | 541.67 | 6500 | 0.5437 | 0.5134 | 0.4705 | 0.6439 | |
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| 0.0043 | 583.33 | 7000 | 0.5393 | 0.5308 | 0.4652 | 0.6414 | |
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| 0.0002 | 625.0 | 7500 | 0.5378 | 0.5572 | 0.4604 | 0.6465 | |
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| 0.0014 | 666.67 | 8000 | 0.5386 | 0.5451 | 0.4591 | 0.6515 | |
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| 0.0027 | 708.33 | 8500 | 0.4629 | 0.6465 | 0.5395 | 0.5747 | |
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| 0.0036 | 750.0 | 9000 | 0.4744 | 0.6540 | 0.5499 | 0.5293 | |
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| 0.0021 | 791.67 | 9500 | 0.4610 | 0.6566 | 0.5417 | 0.5391 | |
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| 0.0002 | 833.33 | 10000 | 0.4625 | 0.6540 | 0.5418 | 0.5359 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.1 |
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