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--- |
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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: pegasus-large-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: 39.1107 |
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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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# pegasus-large-finetuned-Pubmed |
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This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) 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: 1.7669 |
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- Rouge1: 39.1107 |
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- Rouge2: 15.4127 |
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- Rougel: 24.3729 |
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- Rougelsum: 35.1236 |
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- Gen Len: 226.594 |
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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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### 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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| 2.065 | 1.0 | 1000 | 1.8262 | 37.1986 | 14.3685 | 23.7153 | 33.0713 | 218.902 | |
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| 1.9552 | 2.0 | 2000 | 1.7933 | 38.0663 | 14.7813 | 23.8412 | 33.9574 | 217.488 | |
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| 1.8983 | 3.0 | 3000 | 1.7768 | 38.3975 | 15.0983 | 24.0247 | 34.314 | 222.32 | |
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| 1.882 | 4.0 | 4000 | 1.7687 | 39.1311 | 15.4167 | 24.2978 | 35.078 | 222.564 | |
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| 1.8456 | 5.0 | 5000 | 1.7669 | 39.1107 | 15.4127 | 24.3729 | 35.1236 | 226.594 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.9.1 |
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- Datasets 1.18.4 |
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- Tokenizers 0.11.6 |
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