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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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metrics: |
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- rouge |
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model-index: |
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- name: t5-small-finetuned-jb-t5 |
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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-small-finetuned-jb-t5 |
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This model is a fine-tuned version of [t5-small](https://huggingface.co./t5-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0000 |
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- Rouge1: 99.3236 |
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- Rouge2: 99.2849 |
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- Rougel: 99.323 |
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- Rougelsum: 99.3279 |
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- Gen Len: 16.7815 |
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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: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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: 15 |
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- mixed_precision_training: Native AMP |
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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 | 235 | 0.0013 | 99.3122 | 99.2729 | 99.3126 | 99.3178 | 16.7831 | |
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| No log | 2.0 | 470 | 0.0001 | 99.3236 | 99.2849 | 99.323 | 99.3279 | 16.7815 | |
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| 0.0599 | 3.0 | 705 | 0.0000 | 99.3236 | 99.2849 | 99.323 | 99.3279 | 16.7815 | |
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| 0.0599 | 4.0 | 940 | 0.0000 | 99.3236 | 99.2849 | 99.323 | 99.3279 | 16.7815 | |
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| 0.0017 | 5.0 | 1175 | 0.0000 | 99.3236 | 99.2849 | 99.323 | 99.3279 | 16.7815 | |
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| 0.0017 | 6.0 | 1410 | 0.0000 | 99.3236 | 99.2849 | 99.323 | 99.3279 | 16.7815 | |
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| 0.0009 | 7.0 | 1645 | 0.0000 | 99.3236 | 99.2849 | 99.323 | 99.3279 | 16.7815 | |
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| 0.0009 | 8.0 | 1880 | 0.0000 | 99.3236 | 99.2849 | 99.323 | 99.3279 | 16.7815 | |
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| 0.0007 | 9.0 | 2115 | 0.0000 | 99.3236 | 99.2849 | 99.323 | 99.3279 | 16.7815 | |
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| 0.0007 | 10.0 | 2350 | 0.0000 | 99.3236 | 99.2849 | 99.323 | 99.3279 | 16.7815 | |
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| 0.0007 | 11.0 | 2585 | 0.0000 | 99.3236 | 99.2849 | 99.323 | 99.3279 | 16.7815 | |
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| 0.0007 | 12.0 | 2820 | 0.0000 | 99.3236 | 99.2849 | 99.323 | 99.3279 | 16.7815 | |
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| 0.0006 | 13.0 | 3055 | 0.0000 | 99.3236 | 99.2849 | 99.323 | 99.3279 | 16.7815 | |
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| 0.0006 | 14.0 | 3290 | 0.0000 | 99.3236 | 99.2849 | 99.323 | 99.3279 | 16.7815 | |
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| 0.0006 | 15.0 | 3525 | 0.0000 | 99.3236 | 99.2849 | 99.323 | 99.3279 | 16.7815 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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