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--- |
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license: apache-2.0 |
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base_model: t5-small |
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tags: |
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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: t5-small-mlsum |
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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: tu |
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split: None |
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args: tu |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 14.4732 |
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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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# t5-small-mlsum |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the mlsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.6372 |
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- Rouge1: 14.4732 |
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- Rouge2: 6.6752 |
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- Rougel: 13.4183 |
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- Rougelsum: 13.8427 |
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- Gen Len: 19.0 |
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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: 5e-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: 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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| |
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| No log | 1.0 | 13 | 2.7607 | 14.4395 | 6.4679 | 13.2562 | 13.6373 | 19.0 | |
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| No log | 2.0 | 26 | 2.7068 | 14.4214 | 6.4106 | 13.4536 | 13.7502 | 19.0 | |
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| No log | 3.0 | 39 | 2.6689 | 14.7941 | 6.5511 | 13.6862 | 14.1839 | 19.0 | |
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| No log | 4.0 | 52 | 2.6450 | 14.3539 | 6.6061 | 13.281 | 13.7636 | 19.0 | |
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| No log | 5.0 | 65 | 2.6372 | 14.4732 | 6.6752 | 13.4183 | 13.8427 | 19.0 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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