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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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- 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-mt5-small |
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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-mt5-small |
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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: 1.7125 |
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- Rouge1: 0.5005 |
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- Rouge2: 0.1542 |
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- Rougel: 0.4577 |
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- Rougelsum: 0.4587 |
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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: 5.5e-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: 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: 10 |
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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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| 2.7151 | 1.0 | 250 | 2.2431 | 0.3662 | 0.0891 | 0.3557 | 0.3556 | |
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| 2.4198 | 2.0 | 500 | 2.0873 | 0.3997 | 0.1027 | 0.3884 | 0.3883 | |
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| 2.2232 | 3.0 | 750 | 2.0082 | 0.4453 | 0.1309 | 0.4201 | 0.4203 | |
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| 2.0842 | 4.0 | 1000 | 1.9100 | 0.4663 | 0.1467 | 0.4275 | 0.4274 | |
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| 1.9825 | 5.0 | 1250 | 1.8493 | 0.4671 | 0.1457 | 0.4228 | 0.4228 | |
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| 1.9048 | 6.0 | 1500 | 1.7759 | 0.49 | 0.1545 | 0.4503 | 0.4508 | |
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| 1.8606 | 7.0 | 1750 | 1.7438 | 0.4996 | 0.1577 | 0.4575 | 0.4585 | |
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| 1.8208 | 8.0 | 2000 | 1.7236 | 0.4975 | 0.1533 | 0.4555 | 0.4556 | |
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| 1.788 | 9.0 | 2250 | 1.7200 | 0.4983 | 0.156 | 0.4566 | 0.4572 | |
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| 1.7799 | 10.0 | 2500 | 1.7125 | 0.5005 | 0.1542 | 0.4577 | 0.4587 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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