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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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- samsum |
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metrics: |
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- rouge |
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model-index: |
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- name: t5-small-finetuned-samsum-en |
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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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args: samsum |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 42.3215 |
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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-samsum-en |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7863 |
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- Rouge1: 42.3215 |
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- Rouge2: 19.4644 |
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- Rougel: 35.3715 |
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- Rougelsum: 39.1274 |
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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: 10 |
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- eval_batch_size: 10 |
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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: 20 |
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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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| 2.2448 | 1.0 | 300 | 1.8993 | 39.5059 | 17.0654 | 32.9974 | 36.6153 | |
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| 2.0428 | 2.0 | 600 | 1.8499 | 40.0529 | 17.4367 | 33.4804 | 37.057 | |
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| 1.9626 | 3.0 | 900 | 1.8278 | 40.7994 | 17.918 | 34.0773 | 37.6219 | |
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| 1.8992 | 4.0 | 1200 | 1.8118 | 41.3782 | 18.5579 | 34.7794 | 38.4994 | |
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| 1.8429 | 5.0 | 1500 | 1.8006 | 41.8624 | 18.7592 | 34.9262 | 38.7019 | |
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| 1.8057 | 6.0 | 1800 | 1.7988 | 41.1316 | 18.5242 | 34.7271 | 38.2821 | |
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| 1.775 | 7.0 | 2100 | 1.7856 | 42.2036 | 19.3343 | 35.4442 | 39.2114 | |
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| 1.7376 | 8.0 | 2400 | 1.7797 | 41.9569 | 18.9482 | 35.1953 | 38.7609 | |
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| 1.7096 | 9.0 | 2700 | 1.7780 | 42.6065 | 19.2152 | 35.4563 | 39.2736 | |
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| 1.6885 | 10.0 | 3000 | 1.7826 | 42.1595 | 18.8477 | 34.8679 | 38.9388 | |
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| 1.6581 | 11.0 | 3300 | 1.7809 | 42.291 | 19.0846 | 35.1938 | 38.894 | |
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| 1.6392 | 12.0 | 3600 | 1.7824 | 42.3588 | 19.4507 | 35.4588 | 39.2067 | |
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| 1.6258 | 13.0 | 3900 | 1.7806 | 42.0932 | 19.002 | 35.0112 | 38.8053 | |
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| 1.6042 | 14.0 | 4200 | 1.7828 | 42.0564 | 19.3141 | 35.2479 | 38.8301 | |
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| 1.5993 | 15.0 | 4500 | 1.7824 | 42.6056 | 19.5164 | 35.4112 | 39.2322 | |
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| 1.5869 | 16.0 | 4800 | 1.7839 | 42.1505 | 19.1529 | 35.0853 | 38.8788 | |
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| 1.5778 | 17.0 | 5100 | 1.7827 | 42.5416 | 19.5103 | 35.5507 | 39.293 | |
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| 1.5716 | 18.0 | 5400 | 1.7865 | 42.3028 | 19.3783 | 35.3466 | 39.0594 | |
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| 1.5615 | 19.0 | 5700 | 1.7857 | 42.4001 | 19.5111 | 35.4686 | 39.1614 | |
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| 1.5606 | 20.0 | 6000 | 1.7863 | 42.3215 | 19.4644 | 35.3715 | 39.1274 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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