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