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--- |
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license: apache-2.0 |
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base_model: google/mt5-base |
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tags: |
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- summarization |
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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-finetuned-sumeczech |
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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-finetuned-sumeczech |
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This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9291 |
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- Rouge1: 15.9842 |
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- Rouge2: 5.0275 |
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- Rougel: 12.6308 |
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- Rougelsum: 14.0073 |
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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: 0.001 |
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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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:| |
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| 2.6446 | 1.0 | 108450 | 2.4043 | 13.4797 | 3.1596 | 10.6012 | 11.798 | |
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| 2.3864 | 2.0 | 216900 | 2.3327 | 13.955 | 3.387 | 10.9208 | 12.165 | |
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| 2.3381 | 3.0 | 325350 | 2.2699 | 14.2671 | 3.5872 | 11.1539 | 12.4443 | |
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| 2.2583 | 4.0 | 433800 | 2.2085 | 14.5162 | 3.9249 | 11.4167 | 12.697 | |
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| 2.178 | 5.0 | 542250 | 2.1429 | 14.8376 | 4.1524 | 11.6426 | 12.9856 | |
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| 2.0847 | 6.0 | 650700 | 2.0678 | 15.0717 | 4.3497 | 11.8584 | 13.1779 | |
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| 1.9676 | 7.0 | 759150 | 1.9866 | 15.7074 | 4.7106 | 12.3935 | 13.7652 | |
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| 1.8196 | 8.0 | 867600 | 1.9291 | 15.9842 | 5.0275 | 12.6308 | 14.0073 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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