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license: apache-2.0 |
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base_model: emilstabil/mt5-base_V25775 |
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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: mt5-base_V25775_V44105 |
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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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# mt5-base_V25775_V44105 |
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This model is a fine-tuned version of [emilstabil/mt5-base_V25775](https://huggingface.co/emilstabil/mt5-base_V25775) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1019 |
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- Rouge1: 30.2631 |
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- Rouge2: 10.8564 |
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- Rougel: 20.9297 |
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- Rougelsum: 24.8312 |
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- Gen Len: 80.9356 |
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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: 3e-05 |
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- train_batch_size: 3 |
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- eval_batch_size: 3 |
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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: 20 |
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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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| 2.32 | 0.81 | 500 | 2.1435 | 28.7386 | 10.9481 | 20.3975 | 23.8195 | 74.2704 | |
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| 2.2393 | 1.61 | 1000 | 2.1053 | 29.1856 | 10.7042 | 20.4864 | 24.1221 | 75.515 | |
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| 2.2124 | 2.42 | 1500 | 2.1157 | 28.6845 | 10.9397 | 20.4075 | 23.9154 | 74.8627 | |
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| 2.1635 | 3.23 | 2000 | 2.1232 | 28.8373 | 10.8364 | 20.4743 | 24.0269 | 74.1845 | |
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| 2.1148 | 4.03 | 2500 | 2.1149 | 29.0484 | 11.0898 | 20.6711 | 24.0963 | 73.9571 | |
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| 2.0904 | 4.84 | 3000 | 2.1101 | 29.5911 | 11.2027 | 20.883 | 24.3776 | 76.8412 | |
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| 2.0598 | 5.65 | 3500 | 2.1212 | 29.5276 | 10.8551 | 20.5466 | 24.1469 | 78.4506 | |
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| 2.0596 | 6.45 | 4000 | 2.1368 | 29.8832 | 10.9578 | 20.7962 | 24.4686 | 80.3777 | |
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| 2.0135 | 7.26 | 4500 | 2.1173 | 29.5314 | 10.6881 | 20.375 | 24.2483 | 81.5751 | |
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| 2.0085 | 8.06 | 5000 | 2.1050 | 29.7932 | 11.0481 | 20.8481 | 24.5598 | 78.5708 | |
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| 2.0006 | 8.87 | 5500 | 2.1233 | 30.4225 | 11.3125 | 21.1509 | 24.9171 | 81.3648 | |
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| 1.9888 | 9.68 | 6000 | 2.1067 | 29.9013 | 10.7672 | 20.6523 | 24.5878 | 78.7897 | |
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| 1.9496 | 10.48 | 6500 | 2.1036 | 29.7453 | 10.9583 | 20.7396 | 24.3824 | 78.7425 | |
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| 1.9513 | 11.29 | 7000 | 2.1125 | 29.5484 | 10.752 | 20.4861 | 24.3097 | 79.0558 | |
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| 1.9476 | 12.1 | 7500 | 2.1014 | 29.6296 | 10.8252 | 20.6412 | 24.2908 | 76.1202 | |
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| 1.9294 | 12.9 | 8000 | 2.1102 | 29.9456 | 10.9121 | 20.8077 | 24.5787 | 79.515 | |
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| 1.9036 | 13.71 | 8500 | 2.0977 | 30.1173 | 10.9352 | 20.9176 | 24.9725 | 80.9056 | |
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| 1.9415 | 14.52 | 9000 | 2.1011 | 29.9247 | 10.8223 | 20.7609 | 24.6858 | 81.103 | |
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| 1.8959 | 15.32 | 9500 | 2.0998 | 29.8002 | 10.6206 | 20.5674 | 24.6966 | 80.4549 | |
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| 1.9356 | 16.13 | 10000 | 2.1038 | 30.355 | 11.0359 | 21.0347 | 25.0475 | 80.8927 | |
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| 1.8958 | 16.94 | 10500 | 2.1029 | 30.3957 | 11.0562 | 21.1067 | 25.1431 | 82.1588 | |
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| 1.9093 | 17.74 | 11000 | 2.1002 | 30.4669 | 10.9894 | 20.9725 | 24.9598 | 81.1888 | |
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| 1.8969 | 18.55 | 11500 | 2.1045 | 30.4956 | 10.9426 | 20.9578 | 24.9973 | 81.824 | |
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| 1.8971 | 19.35 | 12000 | 2.1019 | 30.2631 | 10.8564 | 20.9297 | 24.8312 | 80.9356 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.1.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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