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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-small-finetuned |
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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 |
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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: 2.3994 |
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- Rouge1: 20.69 |
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- Rouge2: 6.09 |
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- Rougel: 20.15 |
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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: 0.0001 |
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- train_batch_size: 9 |
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- eval_batch_size: 9 |
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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: 40 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| |
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| 4.7204 | 1.45 | 500 | 2.6053 | 16.93 | 4.91 | 16.71 | |
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| 3.1289 | 2.9 | 1000 | 2.4878 | 18.05 | 5.26 | 17.79 | |
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| 2.8862 | 4.35 | 1500 | 2.4109 | 17.45 | 5.06 | 17.04 | |
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| 2.7669 | 5.8 | 2000 | 2.4006 | 18.61 | 5.28 | 18.12 | |
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| 2.6433 | 7.25 | 2500 | 2.4017 | 18.81 | 5.67 | 18.5 | |
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| 2.5514 | 8.7 | 3000 | 2.3917 | 19.5 | 5.88 | 19.09 | |
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| 2.4947 | 10.14 | 3500 | 2.3994 | 20.69 | 6.09 | 20.15 | |
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| 2.3995 | 11.59 | 4000 | 2.3608 | 20.2 | 6.51 | 19.67 | |
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| 2.3798 | 13.04 | 4500 | 2.3251 | 20.1 | 6.25 | 19.71 | |
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| 2.3029 | 14.49 | 5000 | 2.3387 | 19.75 | 6.11 | 19.37 | |
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| 2.2563 | 15.94 | 5500 | 2.3372 | 20.28 | 6.32 | 19.74 | |
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| 2.2109 | 17.39 | 6000 | 2.3410 | 20.67 | 6.38 | 20.13 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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