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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: 3.0279 |
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- Rouge1: 16.4284 |
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- Rouge2: 7.8601 |
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- Rougel: 16.0029 |
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- Rougelsum: 16.0246 |
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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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| 4.4194 | 1.0 | 1209 | 3.3097 | 14.9867 | 6.4886 | 14.4174 | 14.4646 | |
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| 3.8132 | 2.0 | 2418 | 3.1602 | 16.1474 | 7.9815 | 15.5342 | 15.6445 | |
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| 3.5412 | 3.0 | 3627 | 3.0789 | 17.4468 | 8.8014 | 16.9142 | 17.002 | |
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| 3.3861 | 4.0 | 4836 | 3.0775 | 15.903 | 7.4423 | 15.4008 | 15.3871 | |
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| 3.2952 | 5.0 | 6045 | 3.0480 | 15.8646 | 7.3936 | 15.3989 | 15.4395 | |
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| 3.2155 | 6.0 | 7254 | 3.0354 | 16.5887 | 8.0624 | 16.2377 | 16.2562 | |
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| 3.1896 | 7.0 | 8463 | 3.0273 | 17.1092 | 8.5391 | 16.6507 | 16.7272 | |
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| 3.1594 | 8.0 | 9672 | 3.0279 | 16.4284 | 7.8601 | 16.0029 | 16.0246 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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