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--- |
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license: apache-2.0 |
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base_model: google-t5/t5-base |
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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: t5-base-question-answer-summarization |
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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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# t5-base-question-answer-summarization |
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This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1424 |
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- Rouge1: 85.4974 |
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- Rouge2: 77.0571 |
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- Rougel: 82.4125 |
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- Rougelsum: 82.4757 |
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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: 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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| 0.3381 | 1.0 | 526 | 0.1310 | 85.4136 | 77.2307 | 82.5493 | 82.5887 | |
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| 0.1221 | 2.0 | 1052 | 0.1291 | 85.5109 | 77.3495 | 82.5035 | 82.5448 | |
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| 0.1008 | 3.0 | 1578 | 0.1293 | 85.7918 | 77.3841 | 82.5218 | 82.5855 | |
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| 0.0861 | 4.0 | 2104 | 0.1312 | 85.8164 | 77.5711 | 82.5025 | 82.5955 | |
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| 0.075 | 5.0 | 2630 | 0.1358 | 85.769 | 77.3766 | 82.6532 | 82.691 | |
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| 0.069 | 6.0 | 3156 | 0.1361 | 85.417 | 76.9087 | 82.397 | 82.4857 | |
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| 0.0625 | 7.0 | 3682 | 0.1404 | 85.5539 | 77.0784 | 82.4147 | 82.445 | |
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| 0.0595 | 8.0 | 4208 | 0.1424 | 85.4974 | 77.0571 | 82.4125 | 82.4757 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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