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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- samsum |
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metrics: |
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- rouge |
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model-index: |
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- name: flan-T5-base-sum |
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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: samsum |
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type: samsum |
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config: samsum |
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split: test |
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args: samsum |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 47.6617 |
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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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# flan-T5-base-sum |
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This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the samsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3721 |
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- Rouge1: 47.6617 |
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- Rouge2: 23.7647 |
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- Rougel: 40.1155 |
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- Rougelsum: 43.6943 |
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- Gen Len: 17.2759 |
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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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| 1.4403 | 1.0 | 1842 | 1.3822 | 47.2814 | 23.7835 | 39.7427 | 43.4897 | 17.0256 | |
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| 1.3572 | 2.0 | 3684 | 1.3747 | 47.553 | 23.5714 | 39.8212 | 43.6246 | 17.4420 | |
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| 1.2822 | 3.0 | 5526 | 1.3721 | 47.6617 | 23.7647 | 40.1155 | 43.6943 | 17.2759 | |
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| 1.2375 | 4.0 | 7368 | 1.3764 | 47.7453 | 24.1099 | 40.1684 | 43.8659 | 17.2943 | |
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| 1.1935 | 5.0 | 9210 | 1.3780 | 47.614 | 23.6643 | 39.8434 | 43.6558 | 17.3077 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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