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--- |
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license: apache-2.0 |
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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-wikinewssum-portuguese |
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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-wikinewssum-portuguese |
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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: 2.0428 |
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- Rouge1: 9.4966 |
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- Rouge2: 4.2224 |
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- Rougel: 7.9845 |
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- Rougelsum: 8.8641 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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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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| No log | 1.0 | 334 | 2.2258 | 7.3686 | 2.9066 | 6.3167 | 6.8758 | |
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| No log | 2.0 | 668 | 2.1389 | 9.0551 | 3.8395 | 7.6578 | 8.4641 | |
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| No log | 3.0 | 1002 | 2.1030 | 9.2792 | 3.9352 | 7.8259 | 8.663 | |
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| No log | 4.0 | 1336 | 2.0841 | 9.337 | 4.0647 | 7.8662 | 8.693 | |
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| 3.2831 | 5.0 | 1670 | 2.0487 | 9.4244 | 4.0821 | 7.8633 | 8.7111 | |
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| 3.2831 | 6.0 | 2004 | 2.0580 | 9.4598 | 4.1598 | 7.9511 | 8.8299 | |
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| 3.2831 | 7.0 | 2338 | 2.0426 | 9.501 | 4.1885 | 7.9803 | 8.8612 | |
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| 3.2831 | 8.0 | 2672 | 2.0428 | 9.4966 | 4.2224 | 7.9845 | 8.8641 | |
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### Framework versions |
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- Transformers 4.13.0 |
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- Pytorch 1.10.1 |
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- Datasets 1.16.1 |
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- Tokenizers 0.10.3 |
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