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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: gbert-large-finetuned-cust |
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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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# gbert-large-finetuned-cust |
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This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1846 |
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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: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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- num_epochs: 30 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.8251 | 1.0 | 157 | 0.5204 | |
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| 0.508 | 2.0 | 314 | 0.3953 | |
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| 0.4009 | 3.0 | 471 | 0.3242 | |
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| 0.3587 | 4.0 | 628 | 0.3300 | |
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| 0.3276 | 5.0 | 785 | 0.3137 | |
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| 0.302 | 6.0 | 942 | 0.2826 | |
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| 0.2777 | 7.0 | 1099 | 0.2768 | |
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| 0.2609 | 8.0 | 1256 | 0.2726 | |
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| 0.244 | 9.0 | 1413 | 0.2660 | |
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| 0.2274 | 10.0 | 1570 | 0.2391 | |
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| 0.2132 | 11.0 | 1727 | 0.2353 | |
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| 0.2014 | 12.0 | 1884 | 0.2134 | |
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| 0.1835 | 13.0 | 2041 | 0.2278 | |
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| 0.1896 | 14.0 | 2198 | 0.2110 | |
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| 0.1974 | 15.0 | 2355 | 0.2132 | |
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| 0.1775 | 16.0 | 2512 | 0.1973 | |
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| 0.1715 | 17.0 | 2669 | 0.1941 | |
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| 0.1777 | 18.0 | 2826 | 0.2105 | |
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| 0.1741 | 19.0 | 2983 | 0.2127 | |
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| 0.1607 | 20.0 | 3140 | 0.1762 | |
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| 0.1562 | 21.0 | 3297 | 0.2095 | |
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| 0.1548 | 22.0 | 3454 | 0.1805 | |
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| 0.1534 | 23.0 | 3611 | 0.1852 | |
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| 0.1484 | 24.0 | 3768 | 0.1773 | |
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| 0.1473 | 25.0 | 3925 | 0.1759 | |
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| 0.1354 | 26.0 | 4082 | 0.1734 | |
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| 0.136 | 27.0 | 4239 | 0.1902 | |
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| 0.1306 | 28.0 | 4396 | 0.1769 | |
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| 0.1353 | 29.0 | 4553 | 0.1705 | |
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| 0.1368 | 30.0 | 4710 | 0.1846 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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