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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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datasets: |
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- mlsum |
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metrics: |
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- rouge |
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model-index: |
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- name: mt5-small-mlsum_training_sample |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: mlsum |
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type: mlsum |
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config: de |
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split: train |
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args: de |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 28.2078 |
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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-mlsum_training_sample |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the mlsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9727 |
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- Rouge1: 28.2078 |
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- Rouge2: 19.0712 |
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- Rougel: 26.2267 |
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- Rougelsum: 26.9462 |
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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: 0.001 |
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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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| 1.3193 | 1.0 | 6875 | 2.1352 | 25.8941 | 17.4672 | 24.2858 | 24.924 | |
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| 1.2413 | 2.0 | 13750 | 2.0528 | 26.6221 | 18.1166 | 24.8233 | 25.5111 | |
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| 1.1844 | 3.0 | 20625 | 1.9783 | 27.0518 | 18.3457 | 25.2288 | 25.8919 | |
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| 1.0403 | 4.0 | 27500 | 1.9487 | 27.8154 | 18.9701 | 25.9435 | 26.6578 | |
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| 0.9582 | 5.0 | 34375 | 1.9374 | 27.6863 | 18.7723 | 25.7667 | 26.4694 | |
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| 0.8992 | 6.0 | 41250 | 1.9353 | 27.8959 | 18.919 | 26.0434 | 26.7262 | |
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| 0.8109 | 7.0 | 48125 | 1.9492 | 28.0644 | 18.8873 | 26.0628 | 26.757 | |
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| 0.7705 | 8.0 | 55000 | 1.9727 | 28.2078 | 19.0712 | 26.2267 | 26.9462 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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