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--- |
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license: mit |
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base_model: kavg/LiLT-RE-DE |
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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-DE](https://huggingface.co/kavg/LiLT-RE-DE) on the xfun dataset. |
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It achieves the following results on the evaluation set: |
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- Precision: 0.2952 |
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- Recall: 0.4167 |
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- F1: 0.3455 |
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- Loss: 0.3186 |
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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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| 0.1035 | 41.67 | 500 | 0.2905 | 0.1540 | 0.2013 | 0.2291 | |
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| 0.0691 | 83.33 | 1000 | 0.2952 | 0.4167 | 0.3455 | 0.3186 | |
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| 0.0442 | 125.0 | 1500 | 0.2970 | 0.5909 | 0.3953 | 0.2765 | |
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| 0.024 | 166.67 | 2000 | 0.3227 | 0.5884 | 0.4168 | 0.4144 | |
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| 0.0216 | 208.33 | 2500 | 0.3234 | 0.6035 | 0.4211 | 0.4036 | |
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| 0.0096 | 250.0 | 3000 | 0.3534 | 0.6364 | 0.4545 | 0.5716 | |
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| 0.0079 | 291.67 | 3500 | 0.3456 | 0.5934 | 0.4368 | 0.6643 | |
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| 0.0045 | 333.33 | 4000 | 0.3427 | 0.6187 | 0.4410 | 0.6955 | |
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| 0.0017 | 375.0 | 4500 | 0.3587 | 0.6187 | 0.4541 | 0.8144 | |
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| 0.0147 | 416.67 | 5000 | 0.3407 | 0.6212 | 0.4401 | 0.8101 | |
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| 0.0027 | 458.33 | 5500 | 0.3491 | 0.6162 | 0.4457 | 0.8809 | |
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| 0.0079 | 500.0 | 6000 | 0.3183 | 0.6061 | 0.4174 | 0.8863 | |
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| 0.0028 | 541.67 | 6500 | 0.3506 | 0.5985 | 0.4422 | 0.9944 | |
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| 0.0075 | 583.33 | 7000 | 0.3476 | 0.5960 | 0.4391 | 0.9920 | |
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| 0.0002 | 625.0 | 7500 | 0.3448 | 0.6061 | 0.4396 | 0.9752 | |
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| 0.0025 | 666.67 | 8000 | 0.3456 | 0.6162 | 0.4428 | 0.9866 | |
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| 0.0037 | 708.33 | 8500 | 0.3465 | 0.6187 | 0.4442 | 1.0153 | |
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| 0.0041 | 750.0 | 9000 | 0.3442 | 0.6136 | 0.4410 | 1.1227 | |
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| 0.0023 | 791.67 | 9500 | 0.3450 | 0.6237 | 0.4442 | 1.0995 | |
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| 0.0007 | 833.33 | 10000 | 0.3408 | 0.6162 | 0.4388 | 1.1097 | |
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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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