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--- |
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license: apache-2.0 |
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base_model: google/mt5-small |
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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-small-finetuned-amazon-en-es |
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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-small-finetuned-amazon-en-es |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.0348 |
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- Rouge1: 17.5116 |
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- Rouge2: 8.5034 |
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- Rougel: 17.2199 |
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- Rougelsum: 17.0937 |
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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: 5.6e-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: 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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| 6.6706 | 1.0 | 1209 | 3.3114 | 13.1025 | 4.546 | 12.6485 | 12.5756 | |
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| 3.9103 | 2.0 | 2418 | 3.1923 | 16.1192 | 8.3043 | 15.7004 | 15.6132 | |
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| 3.5937 | 3.0 | 3627 | 3.0927 | 17.9684 | 9.2115 | 17.5115 | 17.4098 | |
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| 3.4088 | 4.0 | 4836 | 3.0605 | 17.8543 | 8.4785 | 17.2866 | 17.1586 | |
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| 3.3178 | 5.0 | 6045 | 3.0501 | 16.029 | 7.6078 | 15.5929 | 15.4296 | |
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| 3.2438 | 6.0 | 7254 | 3.0422 | 16.645 | 8.345 | 16.3489 | 16.1929 | |
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| 3.2005 | 7.0 | 8463 | 3.0404 | 16.6148 | 7.5599 | 16.2553 | 16.1232 | |
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| 3.1792 | 8.0 | 9672 | 3.0348 | 17.5116 | 8.5034 | 17.2199 | 17.0937 | |
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### Framework versions |
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- Transformers 4.33.0.dev0 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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