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update model card README.md
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README.md
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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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- pub_med_summarization_dataset
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metrics:
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- rouge
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model-index:
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- name: t5-small-finetuned-pubmed
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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: pub_med_summarization_dataset
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type: pub_med_summarization_dataset
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args: document
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metrics:
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- name: Rouge1
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type: rouge
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value: 8.8295
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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-pubmed
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the pub_med_summarization_dataset dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.2635
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- Rouge1: 8.8295
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- Rouge2: 3.2594
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- Rougel: 7.9975
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- Rougelsum: 8.4483
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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: 2e-05
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- train_batch_size: 2
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- eval_batch_size: 2
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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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- 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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| 2.5892 | 1.0 | 4000 | 2.3616 | 10.1169 | 3.9666 | 8.8854 | 9.5836 | 19.0 |
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| 2.559 | 2.0 | 8000 | 2.3045 | 9.4321 | 3.5398 | 8.424 | 8.984 | 19.0 |
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| 2.5029 | 3.0 | 12000 | 2.2820 | 9.1658 | 3.3686 | 8.2222 | 8.7311 | 19.0 |
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| 2.4673 | 4.0 | 16000 | 2.2692 | 8.8973 | 3.2617 | 8.0395 | 8.5046 | 19.0 |
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| 2.4331 | 5.0 | 20000 | 2.2635 | 8.8295 | 3.2594 | 7.9975 | 8.4483 | 19.0 |
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### Framework versions
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- Transformers 4.17.0
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- Pytorch 1.10.0+cu111
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- Datasets 1.18.3
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- Tokenizers 0.11.6
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