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--- |
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library_name: transformers |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.2830 |
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- Rouge1: 15.06 |
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- Rouge2: 6.78 |
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- Rougel: 14.55 |
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- Rougelsum: 14.52 |
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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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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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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| No log | 1.0 | 125 | 3.2940 | 14.93 | 6.52 | 14.68 | 14.68 | |
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| No log | 2.0 | 250 | 3.2848 | 14.36 | 6.32 | 13.93 | 13.9 | |
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| No log | 3.0 | 375 | 3.2891 | 14.01 | 6.4 | 13.68 | 13.61 | |
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| No log | 4.0 | 500 | 3.2786 | 14.26 | 6.35 | 13.87 | 13.83 | |
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| No log | 5.0 | 625 | 3.2825 | 14.55 | 5.54 | 14.17 | 14.14 | |
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| No log | 6.0 | 750 | 3.2934 | 14.16 | 6.38 | 13.81 | 13.78 | |
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| No log | 7.0 | 875 | 3.2830 | 14.83 | 6.67 | 14.43 | 14.39 | |
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| No log | 8.0 | 1000 | 3.2830 | 15.06 | 6.78 | 14.55 | 14.52 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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