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