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--- |
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license: mit |
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base_model: nielsr/lilt-xlm-roberta-base |
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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 [nielsr/lilt-xlm-roberta-base](https://huggingface.co./nielsr/lilt-xlm-roberta-base) on the xfun dataset. |
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It achieves the following results on the evaluation set: |
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- Precision: 0.2199 |
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- Recall: 0.5308 |
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- F1: 0.3109 |
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- Loss: 0.1355 |
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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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| 0.1811 | 16.67 | 500 | 0 | 0 | 0 | 0.2498 | |
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| 0.1861 | 33.33 | 1000 | 0.4207 | 0.0457 | 0.0825 | 0.2486 | |
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| 0.0373 | 50.0 | 1500 | 0.4845 | 0.0517 | 0.0934 | 0.2116 | |
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| 0.0856 | 66.67 | 2000 | 0.3184 | 0.1504 | 0.2043 | 0.1696 | |
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| 0.0867 | 83.33 | 2500 | 0.2201 | 0.3472 | 0.2694 | 0.1691 | |
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| 0.0832 | 100.0 | 3000 | 0.2369 | 0.3685 | 0.2884 | 0.1687 | |
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| 0.0756 | 116.67 | 3500 | 0.2152 | 0.4301 | 0.2869 | 0.1561 | |
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| 0.0454 | 133.33 | 4000 | 0.2075 | 0.4818 | 0.2900 | 0.1513 | |
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| 0.0629 | 150.0 | 4500 | 0.2202 | 0.5282 | 0.3108 | 0.1748 | |
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| 0.0503 | 166.67 | 5000 | 0.2058 | 0.5573 | 0.3006 | 0.1832 | |
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| 0.05 | 183.33 | 5500 | 0.2263 | 0.5381 | 0.3186 | 0.1863 | |
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| 0.0365 | 200.0 | 6000 | 0.2233 | 0.5712 | 0.3211 | 0.1524 | |
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| 0.0366 | 216.67 | 6500 | 0.2219 | 0.5699 | 0.3195 | 0.1254 | |
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| 0.0352 | 233.33 | 7000 | 0.2199 | 0.5308 | 0.3109 | 0.1355 | |
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| 0.0524 | 250.0 | 7500 | 0.2184 | 0.5500 | 0.3126 | 0.1456 | |
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| 0.0375 | 266.67 | 8000 | 0.2158 | 0.5401 | 0.3084 | 0.1549 | |
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| 0.0324 | 283.33 | 8500 | 0.2068 | 0.5507 | 0.3007 | 0.1840 | |
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| 0.0168 | 300.0 | 9000 | 0.2155 | 0.5520 | 0.3100 | 0.1686 | |
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| 0.0306 | 316.67 | 9500 | 0.2160 | 0.5666 | 0.3128 | 0.1558 | |
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| 0.0229 | 333.33 | 10000 | 0.2152 | 0.5712 | 0.3126 | 0.1633 | |
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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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