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--- |
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license: apache-2.0 |
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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-large-gramatika161k-b16-e10-lr5 |
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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-large-gramatika161k-b16-e10-lr5 |
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This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0909 |
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- Rouge1: 72.6295 |
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- Rouge2: 67.8521 |
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- Rougel: 72.5471 |
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- Rougelsum: 72.5591 |
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- Gen Len: 18.3276 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adafactor |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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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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| 0.9659 | 0.63 | 5000 | 0.1455 | 70.1028 | 63.4969 | 69.9738 | 69.9761 | 18.3378 | |
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| 0.1735 | 1.27 | 10000 | 0.1195 | 71.1156 | 65.2149 | 70.9932 | 71.0038 | 18.3324 | |
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| 0.1391 | 1.9 | 15000 | 0.1076 | 71.5692 | 66.0226 | 71.4676 | 71.472 | 18.3281 | |
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| 0.1149 | 2.54 | 20000 | 0.1035 | 71.8135 | 66.4584 | 71.7212 | 71.7292 | 18.3308 | |
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| 0.1029 | 3.17 | 25000 | 0.0961 | 72.104 | 66.9459 | 72.0139 | 72.0239 | 18.3282 | |
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| 0.0898 | 3.81 | 30000 | 0.0944 | 72.231 | 67.1623 | 72.1412 | 72.1542 | 18.3314 | |
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| 0.0803 | 4.44 | 35000 | 0.0926 | 72.3851 | 67.4624 | 72.3051 | 72.3183 | 18.3286 | |
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| 0.075 | 5.08 | 40000 | 0.0929 | 72.4219 | 67.5102 | 72.3376 | 72.3479 | 18.3298 | |
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| 0.0665 | 5.71 | 45000 | 0.0917 | 72.5132 | 67.6501 | 72.4271 | 72.4383 | 18.3264 | |
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| 0.0624 | 6.35 | 50000 | 0.0911 | 72.5711 | 67.771 | 72.4938 | 72.5041 | 18.3283 | |
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| 0.0588 | 6.98 | 55000 | 0.0909 | 72.6295 | 67.8521 | 72.5471 | 72.5591 | 18.3276 | |
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| 0.0534 | 7.62 | 60000 | 0.0920 | 72.6475 | 67.9046 | 72.5743 | 72.5853 | 18.3278 | |
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| 0.0514 | 8.25 | 65000 | 0.0930 | 72.6373 | 67.894 | 72.5612 | 72.5724 | 18.3277 | |
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| 0.0492 | 8.88 | 70000 | 0.0930 | 72.6593 | 67.9359 | 72.59 | 72.5971 | 18.3273 | |
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| 0.047 | 9.52 | 75000 | 0.0932 | 72.6906 | 68.01 | 72.6172 | 72.6269 | 18.3264 | |
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### Framework versions |
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- Transformers 4.30.1 |
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- Pytorch 1.11.0a0+b6df043 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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