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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: speller-t5-9 |
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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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# speller-t5-9 |
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This model is a fine-tuned version of [sberbank-ai/ruT5-base](https://huggingface.co/sberbank-ai/ruT5-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1614 |
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- Rouge1: 14.9554 |
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- Rouge2: 8.3333 |
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- Rougel: 14.9554 |
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- Rougelsum: 14.9554 |
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- Gen Len: 42.8661 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 1 |
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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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| |
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| 1.0873 | 0.04 | 500 | 0.5259 | 13.7946 | 7.1429 | 13.8393 | 13.8393 | 40.7946 | |
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| 0.6932 | 0.07 | 1000 | 0.3914 | 14.0625 | 8.3333 | 14.0625 | 14.0625 | 43.5357 | |
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| 0.5471 | 0.11 | 1500 | 0.3349 | 13.9633 | 7.9507 | 13.8641 | 13.9633 | 45.0089 | |
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| 0.5566 | 0.14 | 2000 | 0.2954 | 14.0625 | 8.3333 | 14.0625 | 14.0625 | 43.1429 | |
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| 0.4985 | 0.18 | 2500 | 0.2802 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 44.125 | |
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| 0.5175 | 0.22 | 3000 | 0.2631 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 44.4286 | |
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| 0.4377 | 0.25 | 3500 | 0.2431 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.5893 | |
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| 0.4356 | 0.29 | 4000 | 0.2315 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.9286 | |
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| 0.4052 | 0.32 | 4500 | 0.2258 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 43.2232 | |
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| 0.3888 | 0.36 | 5000 | 0.2179 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.6607 | |
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| 0.3731 | 0.39 | 5500 | 0.2063 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.9196 | |
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| 0.436 | 0.43 | 6000 | 0.2075 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.7589 | |
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| 0.42 | 0.47 | 6500 | 0.1993 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.5446 | |
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| 0.378 | 0.5 | 7000 | 0.2036 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 43.0179 | |
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| 0.3431 | 0.54 | 7500 | 0.1914 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.6875 | |
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| 0.3574 | 0.57 | 8000 | 0.1852 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.7321 | |
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| 0.302 | 0.61 | 8500 | 0.1900 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.7946 | |
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| 0.3081 | 0.65 | 9000 | 0.1807 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.7054 | |
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| 0.3266 | 0.68 | 9500 | 0.1755 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.5714 | |
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| 0.3834 | 0.72 | 10000 | 0.1726 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.8482 | |
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| 0.2802 | 0.75 | 10500 | 0.1736 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.8036 | |
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| 0.3013 | 0.79 | 11000 | 0.1675 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.7054 | |
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| 0.3404 | 0.83 | 11500 | 0.1630 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.6786 | |
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| 0.2945 | 0.86 | 12000 | 0.1627 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.6607 | |
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| 0.2819 | 0.9 | 12500 | 0.1633 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.7321 | |
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| 0.3028 | 0.93 | 13000 | 0.1597 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.6429 | |
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| 0.3138 | 0.97 | 13500 | 0.1614 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.8661 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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